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Zaki, A., Mitra, P. P., Rasmussen, L. K. & Chatterjee, S. (2019). Estimate exchange over network is good for distributed hard thresholding pursuit. Signal Processing, 156, 1-11
Open this publication in new window or tab >>Estimate exchange over network is good for distributed hard thresholding pursuit
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 1-11Article in journal (Refereed) Published
Abstract [en]

We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Sparse learning, Distributed algorithm, Greedy pursuit algorithm, RIP analysis
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-240686 (URN)10.1016/j.sigpro.2018.10.010 (DOI)000453494200001 ()2-s2.0-85055577903 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Zaki, A., Mitra, P. P., Rasmussen, L. K. & Chatterjee, S. (2019). Estimate exchange over network is good for distributed hard thresholding pursuit. Signal Processing, 156, 1-11
Open this publication in new window or tab >>Estimate exchange over network is good for distributed hard thresholding pursuit
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 1-11Article in journal (Refereed) Published
Abstract [en]

We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Sparse learning, Distributed algorithm, Greedy pursuit algorithm, RIP analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-240987 (URN)10.1016/j.sigpro.2018.10.010 (DOI)000453494200001 ()2-s2.0-85055577903 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2019). On Hilbert transform, analytic signal, and modulation analysis for signals over graphs. Signal Processing, 156, 106-115
Open this publication in new window or tab >>On Hilbert transform, analytic signal, and modulation analysis for signals over graphs
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 106-115Article in journal (Refereed) Published
Abstract [en]

We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a 'one-sided' spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Graph signal processing, Analytic signal, Hilbert transform, Demodulation, Anomaly detection
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-240687 (URN)10.1016/j.sigpro.2018.10.016 (DOI)000453494200011 ()2-s2.0-85056192636 (Scopus ID)
Note

QC 20190109

Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-09Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2019). On Hilbert transform, analytic signal, and modulation analysis for signals over graphs. Signal Processing, 156, 106-115
Open this publication in new window or tab >>On Hilbert transform, analytic signal, and modulation analysis for signals over graphs
2019 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 156, p. 106-115Article in journal (Refereed) Published
Abstract [en]

We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a 'one-sided' spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to highlight anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that graph analytic signal, and associated amplitude and frequency modulations reveal complementary information in speech signals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Graph signal processing, Analytic signal, Hilbert transform, Demodulation, Anomaly detection
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-240988 (URN)10.1016/j.sigpro.2018.10.016 (DOI)000453494200011 ()2-s2.0-85056192636 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Liang, X., Javid, A. M., Skoglund, M. & Chatterjee, S. (2018). DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENCE. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (pp. 2976-2980). IEEE
Open this publication in new window or tab >>DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENCE
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 2976-2980Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Distributed learning, neural networks, data parallelism, convex optimization
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-237152 (URN)10.1109/ICASSP.2018.8462179 (DOI)000446384603029 ()2-s2.0-85054237028 (Scopus ID)
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2018). Extreme learning machine for graph signal processing. In: 2018 26th European Signal Processing Conference (EUSIPCO): . Paper presented at 26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 3 September 2018 through 7 September 2018 (pp. 136-140). European Signal Processing Conference, EUSIPCO, Article ID 8553088.
Open this publication in new window or tab >>Extreme learning machine for graph signal processing
2018 (English)In: 2018 26th European Signal Processing Conference (EUSIPCO), European Signal Processing Conference, EUSIPCO , 2018, p. 136-140, article id 8553088Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.

Place, publisher, year, edition, pages
European Signal Processing Conference, EUSIPCO, 2018
Series
European Signal Processing Conference, ISSN 2219-5491
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-241525 (URN)10.23919/EUSIPCO.2018.8553088 (DOI)000455614900028 ()2-s2.0-85059801757 (Scopus ID)9789082797015 (ISBN)
Conference
26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 3 September 2018 through 7 September 2018
Note

QC 20180123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-02-01Bibliographically approved
Venkitaraman, A., Chatterjee, S. & Händel, P. (2018). MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (pp. 4644-4648). IEEE
Open this publication in new window or tab >>MULTI-KERNEL REGRESSION FOR GRAPH SIGNAL PROCESSING
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 4644-4648Conference paper, Published paper (Refereed)
Abstract [en]

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We show that the resulting optimization problem is shown to be convex and propose an accelerated projected gradient descent based solution. Simulation results using real-world graph signals show efficiency of the multi-kernel based approach over a standard kernel based approach.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Graph signal processing, kernel regression, convex optimization
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-237154 (URN)10.1109/ICASSP.2018.8461643 (DOI)000446384604162 ()2-s2.0-85054280684 (Scopus ID)
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved
Ghayem, F., Sadeghi, M., Babaie-Zadeh, M., Chatterjee, S., Skoglund, M. & Jutten, C. (2018). Sparse Signal Recovery Using Iterative Proximal Projection. IEEE Transactions on Signal Processing, 66(4), 879-894
Open this publication in new window or tab >>Sparse Signal Recovery Using Iterative Proximal Projection
Show others...
2018 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, no 4, p. 879-894Article in journal (Refereed) Published
Abstract [en]

This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify that the proposed algorithm considerably outperforms some well-known and recently proposed algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Sparse signal recovery, compressed sensing, SL0, proximal splitting algorithms, iterative sparsification-projection
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223260 (URN)10.1109/TSP.2017.2778695 (DOI)000423703600003 ()2-s2.0-85037644363 (Scopus ID)
Note

QC 20180216

Available from: 2018-02-16 Created: 2018-02-16 Last updated: 2018-02-16Bibliographically approved
Sundin, M., Venkitaraman, A., Jansson, M. & Chatterjee, S. (2017). A Connectedness Constraint for Learning Sparse Graphs. In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE (pp. 151-155). IEEE
Open this publication in new window or tab >>A Connectedness Constraint for Learning Sparse Graphs
2017 (English)In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2017, p. 151-155Conference paper, Published paper (Refereed)
Abstract [en]

Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a graph highly sparse can split the graph into several disconnected components, leading to several separate networks. The main difficulty is that connectedness is often treated as a combinatorial property, making it hard to enforce in e.g. convex optimization problems. In this article, we show how connectedness of undirected graphs can be formulated as an analytical property and can be enforced as a convex constraint. We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning. Using simulated and real data, we perform experiments to learn sparse and connected graphs from data.

Place, publisher, year, edition, pages
IEEE, 2017
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-226274 (URN)000426986000031 ()2-s2.0-85041483337 (Scopus ID)978-0-9928-6267-1 (ISBN)
Conference
25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE
Note

QC 20180419

Available from: 2018-04-19 Created: 2018-04-19 Last updated: 2018-04-19Bibliographically approved
Zaki, A., Venkitaraman, A., Chatterjee, S. & Rasmussen, L. K. (2017). Distributed Greedy Sparse Learning over Doubly Stochastic Networks. In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE (pp. 361-364). IEEE
Open this publication in new window or tab >>Distributed Greedy Sparse Learning over Doubly Stochastic Networks
2017 (English)In: 2017 25TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2017, p. 361-364Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we develop a greedy algorithm for sparse learning over a doubly stochastic network. In the proposed algorithm, nodes of the network perform sparse learning by exchanging their individual intermediate variables. The algorithm is iterative in nature. We provide a restricted isometry property (RIP)-based theoretical guarantee both on the performance of the algorithm and the number of iterations required for convergence. Using simulations, we show that the proposed algorithm provides good performance.

Place, publisher, year, edition, pages
IEEE, 2017
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-226275 (URN)10.23919/EUSIPCO.2017.8081229 (DOI)000426986000073 ()2-s2.0-85041494941 (Scopus ID)978-0-9928-6267-1 (ISBN)
Conference
25th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2017, GREECE
Note

QC 20180420

Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-12-05Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2638-6047

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