Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
407151 | Neurocomputing | 2016 | 10 Pages |
Abstract
Recently, sparse coding has been widely adopted for data representation in real-world applications. In order to consider the geometric structure of data, we propose a novel method, local and global regularized sparse coding (LGSC), for data representation. LGSC not only models the global geometric structure by a global regression regularizer, but also takes into account the manifold structure using a local regression regularizer. Compared with traditional sparse coding methods, the proposed method can preserve both global and local geometric structures of the original high-dimensional data in a new representation space. Experimental results on benchmark datasets show that the proposed method can improve the performance of clustering.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhenqiu Shu, Jun Zhou, Pu Huang, Xun Yu, Zhangjing Yang, Chunxia Zhao,