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  • 1.
    Ma, Zhanyu
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Bayesian estimation of the Dirichlet distribution with expectation propagation2012In: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, IEEE Computer Society, 2012, p. 689-693Conference paper (Refereed)
    Abstract [en]

    As a member of the exponential family, the Dirichlet distribution has its conjugate prior. However, since the posterior distribution is difficult to use in practical problems, Bayesian estimation of the Dirichlet distribution, in general, is not analytically tractable. To derive practically easily used prior and posterior distributions, some approximations are required to approximate both the prior and the posterior distributions so that the conjugate match between the prior and posterior distributions holds and the obtained posterior distribution is easy to be employed. To this end, we approximate the distribution of the parameters in the Dirichlet distribution by a multivariate Gaussian distribution, based on the expectation propagation (EP) framework. The EP-based method captures the correlations among the parameters and provides an easily used prior/posterior distribution. Compared to recently proposed Bayesian estimation based on the variation inference (VI) framework, the EP-based method performs better with a smaller amount of observed data and is more stable.

  • 2.
    Ma, Zhanyu
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Non-Gaussian Statistical Modelsand Their Applications2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Statistical modeling plays an important role in various research areas. It provides away to connect the data with the statistics. Based on the statistical properties of theobserved data, an appropriate model can be chosen that leads to a promising practicalperformance. The Gaussian distribution is the most popular and dominant probabilitydistribution used in statistics, since it has an analytically tractable Probability DensityFunction (PDF) and analysis based on it can be derived in an explicit form. However,various data in real applications have bounded support or semi-bounded support. As the support of the Gaussian distribution is unbounded, such type of data is obviously notGaussian distributed. Thus we can apply some non-Gaussian distributions, e.g., the betadistribution, the Dirichlet distribution, to model the distribution of this type of data.The choice of a suitable distribution is favorable for modeling efficiency. Furthermore,the practical performance based on the statistical model can also be improved by a bettermodeling.

    An essential part in statistical modeling is to estimate the values of the parametersin the distribution or to estimate the distribution of the parameters, if we consider themas random variables. Unlike the Gaussian distribution or the corresponding GaussianMixture Model (GMM), a non-Gaussian distribution or a mixture of non-Gaussian dis-tributions does not have an analytically tractable solution, in general. In this dissertation,we study several estimation methods for the non-Gaussian distributions. For the Maxi-mum Likelihood (ML) estimation, a numerical method is utilized to search for the optimalsolution in the estimation of Dirichlet Mixture Model (DMM). For the Bayesian analysis,we utilize some approximations to derive an analytically tractable solution to approxi-mate the distribution of the parameters. The Variational Inference (VI) framework basedmethod has been shown to be efficient for approximating the parameter distribution byseveral researchers. Under this framework, we adapt the conventional Factorized Approx-imation (FA) method to the Extended Factorized Approximation (EFA) method and useit to approximate the parameter distribution in the beta distribution. Also, the LocalVariational Inference (LVI) method is applied to approximate the predictive distributionof the beta distribution. Finally, by assigning a beta distribution to each element in thematrix, we proposed a variational Bayesian Nonnegative Matrix Factorization (NMF) forbounded support data.

    The performances of the proposed non-Gaussian model based methods are evaluatedby several experiments. The beta distribution and the Dirichlet distribution are appliedto model the Line Spectral Frequency (LSF) representation of the Linear Prediction (LP)model for statistical model based speech coding. For some image processing applications,the beta distribution is also applied. The proposed beta distribution based variationalBayesian NMF is applied for image restoration and collaborative filtering. Comparedto some conventional statistical model based methods, the non-Gaussian model basedmethods show a promising improvement.

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  • 3.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    A model-based collaborative filtering method for bounded support data2012In: Proceedings - 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2012, IEEE , 2012, p. 545-548Conference paper (Refereed)
    Abstract [en]

    Collaborative filtering (CF) is an important technique used in some recommendation systems. The task of CF is to estimate the persons' preferences (e.g., ratings) or to predict the preferences for the future, based on some already known persons' preferences. In general, the model-based CF performs better than the memory-based CF, especially for highly sparse data. In this paper, we present a new model-based CF method for bounded support data, which takes into account the facts that the ratings are usually in a limited interval. A nonnegative matrix factorization (NMF) model is applied to investigate and learn the patterns hidden in the observed data matrix. Each rating value is assumed to be beta distributed and we assign the gamma prior to the parameters in a beta distribution for the purpose of Bayesian estimation. With variation inference framework and some lower bound approximations, an analytically tractable solution can be obtained for the proposed NMF model. By comparing with several existing low-rank matrix approximation methods, the good performance of the proposed method is demonstrated.

  • 4.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    A Probabilistic Principal Component Analysis Based Hidden Markov Model For Audio-Visual Speech Recognition.2008In: CONF REC ASILOMAR CONF SIGNAL, 2008, p. 2170-2173Conference paper (Refereed)
    Abstract [en]

    Lipreading is an efficient method among those proposed to improve the performance of speech recognition systems, especially in acoustic noisy environments. This paper proposes a simple audio-visual speech recognition (AVSR) system, which could improve the robustness and accuracy of audio speech recognition by integrating the synchronous audio and visual information. We propose a hidden Markov model (HMM) based on the probabilistic principal component analysis (PCA) for the visual-only speech recognition and the visual modality of the audio-visual speech recognition. The probabilistic PCA based HMM directly uses the images which only contain the speaker's mouth region without pre-processing (mouth corner detection, contour marking, etc), and takes probabilistic PCA as the observation probability density function (PDF). Then we integrate these two modalities information (audio and visual) together and obtain a multi-stream hidden Markov model (MSHMM). We found that, without extracting the specialized features before processing, probabilistic PCA could capture the principal components during the training and describe the visual part of the materials. It is also verified by the experiments that the integration of the audio and visual information could help to improve the recognition accuracy even at a low acoustic signal-to-noisy ratio (SNR).

  • 5.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Approximating the predictive distribution of the beta distribution with the local variational method2011In: IEEE Intl. Workshop on Machine Learning for Signal Processing, 2011Conference paper (Refereed)
    Abstract [en]

    In the Bayesian framework, the predictive distribution is obtained by averaging over the posterior parameter distribution. When there is a small amount of data, the uncertainty of the parameters is high. Thus with the predictive distribution, a more reliable result can be obtained in the applications as classification, recognition, etc. In the previous works, we have utilized the variational inference framework to approximate the posterior distribution of the parameters in the beta distribution by minimizing the Kullback-Leibler divergence of the true posterior distribution from the approximating one. However, the predictive distribution of the beta distribution was approximated by a plug-in approximation with the posterior mean, regardless of the parameter uncertainty. In this paper, we carry on the factorized approximation introduced in the previous work and approximate the beta function by its first order Taylor expansion. Then the upper bound of the predictive distribution is derived by exploiting the local variational method. By minimizing the upper bound of the predictive distribution and after normalization, we approximate the predictive distribution by a probability density function in a closed form. Experimental results shows the accuracy and efficiency of the proposed approximation method.

  • 6.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Bayesian Estimation of Beta Mixture Models with Variational Inference2011In: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 33, no 11, p. 2160-2173Article in journal (Refereed)
    Abstract [en]

    Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutionsto simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation tothe prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed-form) Bayesianapproach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles ofthe VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate thedistribution of the parameters in BMM. In a fully Bayesian model where all the parameters of the BMM are considered as variables andassigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also,the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numericalcalculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximizationalgorithm. The good performance of this approach is verified by experiments with both synthetic and real data.

  • 7.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Beta Mixture Models And The Application To Image Classification2009In: 2009 16th IEEE International Conference On Image Processing, vols 1-6, 2009, p. 2021-2024Conference paper (Refereed)
    Abstract [en]

    Statistical pattern recognition is one of the most studied and applied approaches in the area of pattern recognition. Mixture modelling of densities is an efficient statistical pattern recognition method for continuous data. We propose a classifier based on the beta mixture models for strictly bounded and asymmetrically distributed data. Due to the property of the mixture modelling, the statistical dependence in a multi-dimensional variable is captured, even with the conditional independence assumption in each mixture component. A synthetic example and the USPS handwriting digit data was used to verify the effectiveness of this approach. Compared to the conventional Gaussian mixture models (GMM), the beta mixture models has a better performance on data which has strictly bounded value and asymmetric distribution. The performance of beta mixture models is about equivalent to that of GMM applied to data transformed via a strictly increasing link function.

  • 8.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    BG-NMF: a variational Bayesian NMF model for bounded support data2011Article in journal (Other academic)
    Abstract [en]

    In this paper, we present a new Bayesian nonnegative matrix factor-ization (NMF) method for bounded support data. The distribution of thebounded support data is modelled with the beta distribution. The parametersof the beta density function are considered as latent variables and factorizedinto two matrices (the basis matrix and the excitation matrix). Further-more, each entry in the factorized matrices is assigned with a gamma prior.Thus, we name this method as beta-gamma NMF (BG-NMF). Usually, theestimation of the posterior distribution does not have a closed-form solu-tion. With the variational inference framework and by taking the relativeconvexity property of the log-inverse-beta function, we derive a closed-formsolution to approximate the posterior distribution of the entries in the basisand the excitation matrices. Also, a sparse BG-NMF can be carried outby adding the sparseness constraint to the gamma prior. Evaluations withsynthetic data and real life data demonstrate that the proposed method isefficient for source separation, missing data prediction, and collaborativefiltering problems.

  • 9.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Coding bounded support data with beta distribution2010In: Proceedings - 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2010, 2010, p. 246-250Conference paper (Refereed)
    Abstract [en]

    The probability density function (PDF) optimized quantization has been shown to be more efficient than the conventional quantization methods. In practical application, the data with bounded support can be modelled better with bounded support distribution (e.g. beta distribution, Dirichlet distribution) and a better quantization performance could be achieved by a more reasonable modelling. In this paper, we study the distortion rate (D-R) performance and the high rate quantization performance of the beta distribution. To implement a quantizer efficiently, a practical quantization scheme is proposed. The proposed scheme takes the advantages of conventional compander and exhaustive training. The advantage of the proposed scheme is verified with both theoretical experiment and practical application.

  • 10.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Expectation propagation for estimating the parameters of the beta distribution2010In: 2010 IEEE International Conference On Acoustics, Speech, And Signal Processing, 2010, p. 2082-2085Conference paper (Refereed)
    Abstract [en]

    Parameter estimation for the beta distribution is analytically intractable due to the integration expression in the normalization constant. For maximum likelihood estimation, numerical methods can be used to calculate the parameters. For Bayesian estimation, we can utilize different approximations to the posterior parameter distribution. A method based on the variational inference (VI) framework reported the posterior mean of the parameters analytically but the approximating distribution violated the correlation between the parameters. We now propose a method via the expectation propagation (EP) framework to approximate the posterior distribution analytically and capture the correlation between the parameters. Compared to the method based on VI, the EP based algorithm performs better with small amounts of data and is more stable.

  • 11.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Human Audio-Visual Consonant Recognition Analyzed with Three Bimodal Integration Models2009In: INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5, BAIXAS: ISCA-INST SPEECH COMMUNICATION ASSOC , 2009, p. 812-815Conference paper (Refereed)
    Abstract [en]

    With A-V recordings. ten normal hearing people took recognition tests at different signal-to-noise ratios (SNR). The AV recognition results are predicted by the fuzzy logical model of perception (FLMP) and the post-labelling integration model (POSTL). We also applied hidden Markov models (HMMs) and multi-stream HMMs (MSHMMs) for the recognition. As expected, all the models agree qualitatively with the results that the benefit gained from the visual signal is larger at lower acoustic SNRs. However, the FLMP severely overestimates the AV integration result, while the POSTL model underestimates it. Our automatic speech recognizers integrated the audio and visual stream efficiently. The visual automatic speech recognizer could be adjusted to correspond to human visual performance. The MSHMMs combine the audio and visual streams efficiently, but the audio automatic speech recognizer must be further improved to allow precise quantitative comparisons with human audio-visual performance.

  • 12.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Human skin color detection in RGB space with Bayesian estimation of beta mixture models2010In: EUSIPCO 2010, EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP , 2010, p. 1204-1208Conference paper (Refereed)
    Abstract [en]

    Human skin color detection plays an important role in the applicationsof skin segmentation, face recognition, and tracking. To builda robust human skin color classifier is an essential step. This paperpresents a classifier based on beta mixture models (BMM), whichuses the pixel values in RGB space as the features. We proposea Bayesian estimation method based on the variational inferenceframework to approximate the posterior distribution of the parametersin the BMM and take the posterior mean as a point estimateof the parameters. The well-known Compaq image database is usedto evaluate the performance of our BMM based classifier. Comparedto some other skin color detection methods, our BMM basedclassifier shows a better recognition performance.

  • 13.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Modelling Speech Line Spectral Frequencies with Dirichlet Mixture Models2010In: 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, 2010, p. 2370-2373Conference paper (Refereed)
    Abstract [en]

    In this paper, we model the underlying probability density function(PDF) of the speech line spectral frequencies (LSF) parameterswith a Dirichlet mixture model (DMM). The LSF parametershave two special features: 1) the LSF parameters havea bounded range; 2) the LSF parameters are in an increasingorder. By transforming the LSF parameters to the ΔLSF parameters,the DMM can be used to model the ΔLSF parametersand take advantage of the features mentioned above. Thedistortion-rate (D-R) relation is derived for the Dirichlet distributionwith the high rate assumption. A bit allocation strategyfor DMM is also proposed. In modelling the LSF parametersextracted from the TIMIT database, the DMM shows a betterperformance compared to the Gaussian mixture model, in termsof D-R relation, likelihood and model complexity. Since modellingis the essential and prerequisite step in the PDF-optimizedvector quantizer design, better modelling results indicate a superiorquantization performance.

  • 14.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    PDF-optimized LSF vector quantization based on beta mixture models2010In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010, 2010, p. 2374-2377Conference paper (Refereed)
    Abstract [en]

    The line spectral frequencies (LSF) are known to be the mostefficient representation of the linear predictive coding (LPC) parametersfrom both the distortion and perceptual point of view.By considering the bounded property of the LSF parameters,we apply beta mixture models (BMM) to model the distributionof the LSF parameters. Meanwhile, by following the principlesof probability density function (PDF) optimized vector quantization(VQ), we derive the bit allocation strategy for the BMM.The LSF parameters are obtained from the TIMIT database anda practical VQ is designed. By taking the Bayesian informationcriterion (BIC), the square error (SE) and the spectral distortion(SD) as the criteria, the BMM based VQ outperforms theGaussian mixture model based VQ with uncorrelated Gaussiancomponent (UGMVQ) by about 1-2 bits/vector.

  • 15.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Super-Dirichlet Mixture Models using Differential Line Spectral Frequences for Text-Independent Speaker Identification2011In: INTERSPEECH 2011, 2011Conference paper (Refereed)
    Abstract [en]

    A new text-independent speaker identification (SI) system is proposed. This system utilizes the line spectral frequencies (LSFs) as alternative feature set for capturing the speaker characteristics. The boundary and ordering properties of the LSFs are considered and the LSF are transformed to the differential LSF (DLSF) space. Since the dynamic information is useful for speaker recognition, we represent the dynamic information of the DLSFs by considering two neighbors of the current frame, one from the past frames and the other from the following frames. The current frame with the neighbor frames together are cascaded into a supervector. The statistical distribution of this supervector is modelled by the so-called super-Dirichlet mixture model, which is an extension from the Dirichlet mixture model. Compared to the conventional SI system, which is using the mel-frequency cepstral coefficients and based on the Gaussian mixture model, the proposed SI system shows a promising improvement.

  • 16.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Super-Dirichlet Mixture Models using Differential Line Spectral Frequencies for Text-Independent Speaker Identification2011In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2011, p. 2360-2363Conference paper (Refereed)
    Abstract [en]

    A new text-independent speaker identification (SI) system is proposed. This system utilizes the line spectral frequencies (LSFs) as alternative feature set for capturing the speaker char.: acteristics. The boundary and ordering properties of the LSFs are considered and the LSF are transformed to the differential LSF (DLSF) space. Since the dynamic information is useful for speaker recognition, we represent the dynamic information of the DLSFs by considering two neighbors of the current frame, one from the past frames and the other from the following frames. The current frame with the neighbor frames together are cascaded into a supervector. The statistical distribution of this supervector is modelled by the so-called super-Dirichlet mixture model, which is an extension from the Dirichlet mixture model. Compared to the conventional SI system, which is using the mel-frequency cepstral coefficients and based on the Gaussian mixture model, the proposed SI system shows a promising improvement.

  • 17.
    Ma, Zhanyu
    et al.
    Beijing University of Posts and Telecommunications, China.
    Leijon, Arne
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Kleijn, W. Bastiaan
    School of Engineering and Computer Science, Victoria University of Wellington, New Zealand.
    Vector Quantization of LSF Parameters With a Mixture of Dirichlet Distributions2013In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, E-ISSN 1558-7924, Vol. 21, no 9, p. 1777-1790Article in journal (Refereed)
    Abstract [en]

    Quantization of the linear predictive coding parameters is an important part in speech coding. Probability density function (PDF)-optimized vector quantization (VQ) has been previously shown to be more efficient than VQ based only on training data. For data with bounded support, some well-defined bounded-support distributions (e.g., the Dirichlet distribution) have been proven to outperform the conventional Gaussian mixture model (GMM), with the same number of free parameters required to describe the model. When exploiting both the boundary and the order properties of the line spectral frequency (LSF) parameters, the distribution of LSF differences (Delta LSF) can be modelled with a Dirichlet mixture model (DMM). We propose a corresponding DMM based VQ. The elements in a Dirichlet vector variable are highly mutually correlated. Motivated by the Dirichlet vector variable's neutrality property, a practical non-linear transformation scheme for the Dirichlet vector variable can be obtained. Similar to the Karhunen-Loeve transform for Gaussian variables, this non-linear transformation decomposes the Dirichlet vector variable into a set of independent beta-distributed variables. Using high rate quantization theory and by the entropy constraint, the optimal inter-and intra-component bit allocation strategies are proposed. In the implementation of scalar quantizers, we use the constrained-resolution coding to approximate the derived constrained-entropy coding. A practical coding scheme for DVQ is designed for the purpose of reducing the quantization error accumulation. The theoretical and practical quantization performance of DVQ is evaluated. Compared to the state-of-the-art GMM-based VQ and recently proposed beta mixture model (BMM) based VQ, DVQ performs better, with even fewer free parameters and lower computational cost.

  • 18.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Sun, Qie
    KTH, School of Industrial Engineering and Management (ITM), Industrial Ecology.
    Li, Hailong
    Mälardalen University, Sweden.
    Wang, Chao
    Tongji University, China.
    Yan, Aibin
    Tianjin Institute of Urban Construction, China.
    Starfelt, Fredrik
    Mälardalen University, Sweden.
    Dynamic Prediction of the Heat Demand for Buildings in District Heating Systems2013In: Proceedings of the 5th International Conference on Applied Energy, 2013Conference paper (Refereed)
    Abstract [en]

    Heat demand is a key parameter for optimizing district heating (DH) systems. A mathematical model employing the Gaussian mixture model (GMM) was developed in order to predict the heat demand in DH systems on the consumer side. Prediction of heat demands needs to consider outdoor temperature and people’s social behaviors. In order to precisely consider the effects of social behaviors, the buildings in DH systems were classified into three types: commercial buildings, office buildings and apartment buildings. The model was trained and validated based on the water flow rate and the temperatures of supply and return water in the DH system. The results showed that the model can predict the heat demand in DH systems with an uncertainty between 4-9%. According to the heat demand predicted by the developed model, the potentials of energy saving in the DH system were analyzed.

  • 19.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Tan, Zheng-Hua
    Aalborg University, Denmark.
    Prasad, Swati
    Aalborg University, Denmark.
    EEG Signal Classification with Super-Dirichlet Mixture Model2012In: Processings of IEEE Statistical Signal Processing (SSP) Workshop 2012, IEEE , 2012, p. 440-443Conference paper (Refereed)
    Abstract [en]

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the transient nature of the signals. To improve the classification performance based on the mDWT coefficients, we propose a new classification method by utilizing the nonnegative and sum-to-one properties of the mDWT coefficients. To this end, the distribution of the mDWT coefficients is modeled by the Dirichlet distribution and the distribution of the mDWT coefficients from more than one channels is described by a super-Dirichletmixture model (SDMM). The Fisher ratio and the generalization error estimation are applied to select relevant channels, respectively. Compared to the state-of-the-art support vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies.

  • 20.
    Ma, Zhanyu
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Teschendorff, Andrew E.
    Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London.
    A variational bayes beta mixture model for feature selection in DNA methylation studies2013In: Journal of Bioinformatics and Computational Biology, ISSN 0219-7200, E-ISSN 1757-6334, Vol. 11, no 4, p. 1350005-Article in journal (Refereed)
    Abstract [en]

    An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.

  • 21.
    Rana, Pravin Kumar
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Ma, Zhanyu
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Taghia, Jalil
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Flierl, Markus
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Multiview Depth Map Enhancement by Variational Bayes Inference Estimation of Dirichlet Mixture Models2013In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE , 2013, p. 1528-1532Conference paper (Refereed)
    Abstract [en]

    High quality view synthesis is a prerequisite for future free-viewpointtelevision. It will enable viewers to move freely in a dynamicreal world scene. Depth image based rendering algorithms willplay a pivotal role when synthesizing an arbitrary number of novelviews by using a subset of captured views and corresponding depthmaps only. Usually, each depth map is estimated individually bystereo-matching algorithms and, hence, shows lack of inter-viewconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the inter-view consistency ofmultiview depth imagery. First, our approach classifies the colorinformation in the multiview color imagery by modeling color witha mixture of Dirichlet distributions where the model parameters areestimated in a Bayesian framework with variational inference. Second, using the resulting color clusters, we classify the correspondingdepth values in the multiview depth imagery. Each clustered depthimage is subject to further sub-clustering. Finally, the resultingmean of each sub-cluster is used to enhance the depth imagery atmultiple viewpoints. Experiments show that our approach improvesthe average quality of virtual views by up to 0.8 dB when comparedto views synthesized by using conventionally estimated depth maps.

1 - 21 of 21
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  • asciidoc
  • rtf