TY - JOUR
T1 - Toward Efficient Image Representation
T2 - Sparse Concept Discriminant Matrix Factorization
AU - Pang, Meng
AU - CHEUNG, Yiu Ming
AU - Liu, Risheng
AU - Lou, Jian
AU - Lin, Chuang
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61672444, Grant 61272366, and Grant 61672125, in part by the SZSTI Grant JCYJ20160531194006833, in part by the Faculty Research Grant of Hong Kong Baptist University under Project FRG2/17-18/082, and in part by the Fundamental Research Funds for the Central Universities.
PY - 2019/11
Y1 - 2019/11
N2 - The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., L1 -graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.
AB - The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., L1 -graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.
KW - Fisher-like criterion
KW - graph embedding
KW - image representation
KW - Matrix factorization
KW - sparse coding
UR - http://www.scopus.com/inward/record.url?scp=85056303293&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2018.2879833
DO - 10.1109/TCSVT.2018.2879833
M3 - Journal article
AN - SCOPUS:85056303293
SN - 1051-8215
VL - 29
SP - 3184
EP - 3198
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 11
M1 - 8525292
ER -