Paper Info
Title
Semi-Supervised Group Sparse Representation: Model, Algorithm and Applications.
Abstract
Group sparse representation (GSR) exploits group structure in data and works well on many problems. However, the group structure must be manually given in advance. In many practical scenarios such as classification, samples are grouped according to their labels. Constructing a consistent group structure in such cases is not easy. The reasons are: 1) samples may be incorrectly labeled; and 2) label assigning in big data is time-consuming and expensive. In this paper, we propose and formulate a new problem, semi-supervised group sparse representation (SS-GSR) to support group sparse representation among both labeled and unlabeled data, while learning a more robust group structure, which can be further exploited to more effectively represent other unlabeled data. We develop a model to tackle the SS-GSR problem, based on the manifold assumption in subspace segmentation that samples in the same group lie close in feature space and span the same subspace. We also propose an alternating algorithm to solve the model. Finally, we validate the model via extensive experiments.
Year
DOI
Venue
2016
10.3233/978-1-61499-672-9-507
Frontiers in Artificial Intelligence and Applications
Field
DocType
Volume
K-SVD,Computer science,Sparse approximation,Algorithm
Conference
285
ISSN
Citations 
PageRank 
0922-6389
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Longwen Gao1141.69
Yeqing Li224413.01
Junzhou Huang32182141.43
Shuigeng Zhou42089207.00