Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 28, Issue 6, Pages 1263-1275Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2016.2521602
Keywords
Dimensionality reduction; manifold learning; regression; sparse coding
Categories
Funding
- China 973 Program [2013CB329404]
- National Natural Science Foundation of China [61450001, 61263035, 61573270]
- Guangxi Natural Science Foundation [2012GXNSFGA060004, 2015GXNSFCB139011]
- Guangxi 100 Plan
- Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing
- Guangxi Bagui Scholar Teams for Innovation and Research Project
Ask authors/readers for more resources
In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k-nearest neighbor classification performance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available