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Quantized Nonnegative Matrix Factorization
Telecommunications Software and Systems Group, Ireland.ORCID iD: 0000-0002-3912-1470
2014 (English)In: International Conference on Digital Signal Processing, DSP, IEEE , 2014, 1-6 p.Conference paper (Refereed)
Abstract [en]

Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of interest (with in 1-4dB), 2) admits to practical encoding techniques by learning a factorization which is simpler than NMF's (by a factor of 20-70) and 3) exhibits a complexity which is comparable with state-of-the-art NMF methods. These properties are achieved by considering the quantization residual via an outer quantization optimization step, in an extended NMF iteration, namely QNMF. This approach comes in two forms: QNMF with 1) quasi-fixed and 2) adaptive quantization levels. Quantized NMF considers element-wise quantization constraints in the learning algorithm to eliminate defects due to post factorization quantization. We demonstrate significant reduction in the cardinality of the factor signal values set for comparable Signal-to-Noise-Ratios in a matrix decomposition task.

Place, publisher, year, edition, pages
IEEE , 2014. 1-6 p.
Keyword [en]
encoding, iterative methods, learning (artificial intelligence), matrix decomposition, quantisation (signal), Frobenius-norm quantized nonnegative matrix factorization algorithm, QNMF, adaptive quantization levels, decomposition rank of interest, element-wise quantization constraints, encoding techniques, extended NMF iteration, factor signal values set, learning algorithm, matrix decomposition task, outer quantization optimization, post factorization quantization, quantization residual, quasifixed quantization levels, rank reduction, signal compaction, signal-to-noise-ratios, Approximation methods, Convergence, Digital signal processing, Matrix decomposition, Optimization, Quantization (signal), Signal processing algorithms, low rank, nmf, quantization
National Category
Signal Processing
Research subject
Computer Science
URN: urn:nbn:se:kth:diva-173813DOI: 10.1109/ICDSP.2014.6900690ScopusID: 2-s2.0-84940779707ISBN: 978-147994612-9OAI: diva2:854999
2014 19th International Conference on Digital Signal Processing, DSP 2014; Hong Kong

QC 20160310

Available from: 2015-09-18 Created: 2015-09-18 Last updated: 2016-03-10Bibliographically approved

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de Fréin, Ruairí
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