Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICASSP.2019.8683718
Title: MODEL SELECTION FOR NONNEGATIVE MATRIX FACTORIZATION BY SUPPORT UNION RECOVERY
Authors: Liu, Zhaoqiang 
Keywords: Science & Technology
Technology
Acoustics
Engineering, Electrical & Electronic
Engineering
Nonnegative matrix factorization
Model selection
Tensor method
Multiple measurement vector
Support union recovery
Issue Date: 1-Jan-2019
Publisher: IEEE
Citation: Liu, Zhaoqiang (2019-01-01). MODEL SELECTION FOR NONNEGATIVE MATRIX FACTORIZATION BY SUPPORT UNION RECOVERY. 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) 2019-May : 3407-3411. ScholarBank@NUS Repository. https://doi.org/10.1109/ICASSP.2019.8683718
Abstract: © 2019 IEEE. Nonnegative matrix factorization (NMF) has been widely used in machine learning and signal processing because of its non-subtractive, part-based property which enhances interpretability. It is often assumed that the latent dimensionality (or the number of components) is given. Despite the large amount of algorithms designed for NMF, there is little literature about automatic model selection for NMF with theoretical guarantees. In this paper, we propose an algorithm that first calculates an empirical second-order moment from the empirical fourth-order cumulant tensor, and then estimates the latent dimensionality by recovering the support union (the index set of non-zero rows) of a matrix related to the empirical second-order moment. By assuming a generative model of the data with additional mild conditions, our algorithm provably detects the true latent dimensionality. We show on synthetic examples that our proposed algorithm is able to find approximately correct number of components.
Source Title: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
URI: https://scholarbank.nus.edu.sg/handle/10635/171684
ISSN: 15206149
DOI: 10.1109/ICASSP.2019.8683718
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