Discriminant sparse nonnegative matrix factorization

Authors:
Ruicong Zhi;Qiuqi Ruan
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing, P. R. China;Institute of Information Science, Beijing Jiaotong University, Beijing, P. R. China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 3
Cited 1

Comprehensive Database for Facial Expression Analysis

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification

IEEE Transactions on Neural Networks

Manifold based sparse representation for facial understanding in natural images

Image and Vision Computing

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Abstract

In this paper, a novel discriminant sparse non-negative matrix factorization (DSNMF) algorithm is proposed. We derive DSNMF method from original NMF algorithm by considering both sparseness constraint and discriminant information constraint. Furthermore, projected gradient method is used to solve the optimization problem. DSNMF makes use of prior class information which is important in classification, so it is a supervised method. Furthermore, by minimization l1-norm of the basis, we get a sparse representation of the facial images. Experiments are carried out for facial expression recognition. The experimental results obtained on Cohn-Kanade facial expression database indicate that DSNMF is efficient for facial expression recognition.