The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La factorisation matricielle non négative (NMF) est une méthode prometteuse de décomposition matricielle basée sur les données, et devient très active et attrayante dans les domaines de l'apprentissage automatique et de la séparation aveugle de sources. Jusqu'à présent, l'algorithme NMF a été largement utilisé dans diverses applications, notamment le traitement d'images, l'anti-collision pour les systèmes d'identification par radiofréquence (RFID) et l'analyse de signaux audio, etc. Cependant, les algorithmes NMF typiques ne peuvent pas fonctionner correctement dans des mélanges sous-déterminés, c'est-à-dire que le nombre de signaux observés est inférieur à celui des signaux sources. Dans les applications pratiques, l'ajout de contraintes appropriées fusionnées dans l'algorithme NMF peut obtenir des résultats de décomposition remarquables. En guise de motivation, cet article propose d'ajouter les contraintes de volume minimum et de corrélation minimale (MCV) à l'algorithme NMF, ce qui rend le nouvel algorithme appelé algorithme MCV-NMF adapté aux scénarios sous-déterminés dans lesquels les signaux sources satisfont à des hypothèses mutuellement indépendantes. Les résultats de simulation expérimentale confirment que l'algorithme MCV-NMF présente une meilleure amélioration des performances dans la résolution du problème d'anti-collision des étiquettes RFID que celle obtenue en utilisant la méthode NMF typique la plus proche.
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},}
Copier
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 -