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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

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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Zhongqiang LUO, Chaofu JING, Chengjie LI, "Nonnegative Matrix Factorization with Minimum Correlation and Volume Constrains" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 5, pp. 877-881, May 2022, doi: 10.1587/transfun.2021EAL2050.

Abstract: 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.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2050/_p

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@ARTICLE{e105-a_5_877,

author={Zhongqiang LUO, Chaofu JING, Chengjie LI, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Nonnegative Matrix Factorization with Minimum Correlation and Volume Constrains},

year={2022},

volume={E105-A},

number={5},

pages={877-881},

abstract={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.},

keywords={},

doi={10.1587/transfun.2021EAL2050},

ISSN={1745-1337},

month={May},}

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TY - JOUR

TI - Nonnegative Matrix Factorization with Minimum Correlation and Volume Constrains

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 877

EP - 881

AU - Zhongqiang LUO

AU - Chaofu JING

AU - Chengjie LI

PY - 2022

DO - 10.1587/transfun.2021EAL2050

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E105-A

IS - 5

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - May 2022

AB - 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.

ER -