Accession Number:

AD1024502

Title:

Locality-Constrained Discriminative Learning and Coding

Descriptive Note:

Conference Paper

Corporate Author:

Northeastern University Boston United States

Personal Author(s):

Report Date:

2015-06-12

Pagination or Media Count:

10.0

Abstract:

This paper explores the enhancement by locality constraint to both learning and coding schemes, more specifically,discriminative low-rank dictionary learning and auto-encoder. Previous Fisher discriminative based dictionary learning has led to interesting results by learning more discerning sub-dictionaries. Also, the low-rank regularization term has been introduced to take advantage of the global structure of the data. However, such methods fail to consider datas intrinsic manifold structure. To this end, first, we apply locality constraint on dictionary learning to explore whether the identification capability will be enhanced or not by using the geometric structure information. Moreover, inspired by the recent advances from autoencoders for learning compact feature spaces, we propose a locality-constrained collaborative auto-encoder LCAEfor feature extraction. The improvement from applying locality to dictionary learning and auto-encoder is evaluated on several datasets. Experimental results have demonstrated the effectiveness of locality information compared with state-of-the-art methods.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE