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Nonnegative Matrix Factorization with Minimum Correlation and Volume Constrains

Zhongqiang LUO, Chaofu JING, Chengjie LI

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Summary :

Nonnegative Matrix Factorization (NMF) is a promising data-driven matrix decomposition method, and is becoming very active and attractive in machine learning and blind source separation areas. So far NMF algorithm has been widely used in diverse applications, including image processing, anti-collision for Radio Frequency Identification (RFID) systems and audio signal analysis, and so on. However the typical NMF algorithms cannot work well in underdetermined mixture, i.e., the number of observed signals is less than that of source signals. In practical applications, adding suitable constraints fused into NMF algorithm can achieve remarkable decomposition results. As a motivation, this paper proposes to add the minimum volume and minimum correlation constrains (MCV) to the NMF algorithm, which makes the new algorithm named MCV-NMF algorithm suitable for underdetermined scenarios where the source signals satisfy mutual independent assumption. Experimental simulation results validate that the MCV-NMF algorithm has a better performance improvement in solving RFID tag anti-collision problem than that of using the nearest typical NMF method.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.5 pp.877-881
Publication Date
2022/05/01
Publicized
2021/11/22
Online ISSN
1745-1337
DOI
10.1587/transfun.2021EAL2050
Type of Manuscript
LETTER
Category
Digital Signal Processing

Authors

Zhongqiang LUO
  Sichuan University of Science and Engineering
Chaofu JING
  Sichuan University of Science and Engineering
Chengjie LI
  Southwest Minzu University

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