Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2015-09-27

Included Journals: Scopus、CPCI-S、EI

Volume: 2015-December

Page Number: 1255-1259

Key Words: Sparse coding; fisher-like criterion; matrix factorization; manifold learning; image representation

Abstract: Over the past few decades, matrix factorization has attracted considerable attention for image representation. It is desired for a matrix factorization technique to find the basis that is able to capture highly discriminant information as well as to preserve the intrinsic manifold structure. Besides, the basis has to generate a sparse representation for a given image. In this paper, we propose a matrix factorization method called Sparse concept Discriminant Matrix Factorization (SDMF) by combining a novel fisher-like criterion with the sparse coding. The criterion is discriminant enough across different feature spaces, and meanwhile maintains locally neighboring structures. The proposed method is general for both cases with and without class labels, hence yielding supervised and unsupervised SDMFs. Experimental results show that SDMF provides better representation with higher performance on two tasks (image recognition and clustering) compared with the existing matrix factorization methods.