TY - GEN
T1 - Robust sparse nonnegative matrix factorization based on maximum correntropy criterion
AU - Peng, Siyuan
AU - Ser, Wee
AU - Lin, Zhiping
AU - Chen, Badong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - Nonnegative matrix factorization (NMF) is a significant matrix decomposition technique for learning parts-based, linear representation of nonnegative data, which has been widely used in a broad range of practical applications such as document clustering, image clustering, face recognition and blind spectral unmixing. Traditional NMF methods, which mainly minimize the square of the Euclidean distance or the Kullback-Leibler (KL) divergence, seriously suffer the outliers and non-Gaussian noises. In this paper, we propose a robust sparse nonnegative matrix factorization algorithm, called l1-norm nonnegative matrix factorization based on maximum correntropy criterion (11-CNMF). Specifically, l1-CNMF is derived from the traditional NMF algorithm by incorporating the l1 sparsity constraint and maximum correntropy criterion. Numerical experiments on the Yale database and the ORL database with and without apparent outliers show the effectiveness of the proposed algorithm for image clustering compared with other existing related methods.
AB - Nonnegative matrix factorization (NMF) is a significant matrix decomposition technique for learning parts-based, linear representation of nonnegative data, which has been widely used in a broad range of practical applications such as document clustering, image clustering, face recognition and blind spectral unmixing. Traditional NMF methods, which mainly minimize the square of the Euclidean distance or the Kullback-Leibler (KL) divergence, seriously suffer the outliers and non-Gaussian noises. In this paper, we propose a robust sparse nonnegative matrix factorization algorithm, called l1-norm nonnegative matrix factorization based on maximum correntropy criterion (11-CNMF). Specifically, l1-CNMF is derived from the traditional NMF algorithm by incorporating the l1 sparsity constraint and maximum correntropy criterion. Numerical experiments on the Yale database and the ORL database with and without apparent outliers show the effectiveness of the proposed algorithm for image clustering compared with other existing related methods.
UR - https://www.scopus.com/pages/publications/85057095627
U2 - 10.1109/ISCAS.2018.8351104
DO - 10.1109/ISCAS.2018.8351104
M3 - 会议稿件
AN - SCOPUS:85057095627
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Y2 - 27 May 2018 through 30 May 2018
ER -