Solving non-negative matrix factorization by alternating least squares with a modified strategy

被引：21
|
作者：
Liu, Hongwei [1 ]
Li, Xiangli [1 ,2 ]
Zheng, Xiuyun [1 ]
机构：
[1] Xidian Univ, Dept Math, Xian 710071, Peoples R China
[2] Guilin Univ Elect Technol, Coll Math & Comp Sci, Guilin 541004, Peoples R China
来源：
DATA MINING AND KNOWLEDGE DISCOVERY | 2013年 / 26卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Non-negative matrix factorization; Alternating non-negative least squares; ALGORITHMS; DISCOVERY;
D O I：
10.1007/s10618-012-0265-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Non-negative matrix factorization (NMF) is a method to obtain a representation of data using non-negativity constraints. A popular approach is alternating non-negative least squares (ANLS). As is well known, if the sequence generated by ANLS has at least one limit point, then the limit point is a stationary point of NMF. However, no evdience has shown that the sequence generated by ANLS has at least one limit point. In order to overcome this shortcoming, we propose a modified strategy for ANLS in this paper. The modified strategy can ensure the sequence generated by ANLS has at least one limit point, and this limit point is a stationary point of NMF. The results of numerical experiments are reported to show the effectiveness of the proposed algorithm.
引用
收藏
页码：435 / 451
页数：17
相关论文
共 50 条
  • [21] Dropout non-negative matrix factorization
    He, Zhicheng
    Liu, Jie
    Liu, Caihua
    Wang, Yuan
    Yin, Airu
    Huang, Yalou
    KNOWLEDGE AND INFORMATION SYSTEMS, 2019, 60 (02) : 781 - 806
  • [22] Non-negative matrix factorization with α-divergence
    Cichocki, Andrzej
    Lee, Hyekyoung
    Kim, Yong-Deok
    Choi, Seungjin
    PATTERN RECOGNITION LETTERS, 2008, 29 (09) : 1433 - 1440
  • [23] Uniqueness of non-negative matrix factorization
    Laurberg, Hans
    2007 IEEE/SP 14TH WORKSHOP ON STATISTICAL SIGNAL PROCESSING, VOLS 1 AND 2, 2007, : 44 - 48
  • [24] Stretched non-negative matrix factorization
    Gu, Ran
    Rakita, Yevgeny
    Lan, Ling
    Thatcher, Zach
    Kamm, Gabrielle E.
    O'Nolan, Daniel
    Mcbride, Brennan
    Wustrow, Allison
    Neilson, James R.
    Chapman, Karena W.
    Du, Qiang
    Billinge, Simon J. L.
    NPJ COMPUTATIONAL MATERIALS, 2024, 10 (01)
  • [25] Non-negative Matrix Factorization on Manifold
    Cai, Deng
    He, Xiaofei
    Wu, Xiaoyun
    Han, Jiawei
    ICDM 2008: EIGHTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2008, : 63 - +
  • [26] Non-negative Matrix Factorization on GPU
    Platos, Jan
    Gajdos, Petr
    Kroemer, Pavel
    Snasel, Vaclav
    NETWORKED DIGITAL TECHNOLOGIES, PT 1, 2010, 87 : 21 - 30
  • [27] Bayesian Non-negative Matrix Factorization
    Schmidt, Mikkel N.
    Winther, Ole
    Hansen, Lars Kai
    INDEPENDENT COMPONENT ANALYSIS AND SIGNAL SEPARATION, PROCEEDINGS, 2009, 5441 : 540 - +
  • [28] On affine non-negative matrix factorization
    Laurberg, Hans
    Hansen, Lars Kai
    2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PTS 1-3, 2007, : 653 - +
  • [29] Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations
    Li, Yuanzhi
    Liang, Yingyu
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70, 2017, 70
  • [30] Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
    Kim, Hyunsoo
    Park, Haesun
    BIOINFORMATICS, 2007, 23 (12) : 1495 - 1502
12345