Research article summary (published 29 Sep 2009):

Enhancing bilinear subspace learning by element rearrangement.

Full Abstract

The success of bilinear subspace learning heavily depends on reducing correlations among features along rows and columns of the data matrices. In this work, we study the problem of rearranging elements within a matrix in order to maximize these correlations so that information redundancy in matrix data can be more extensively removed by existing bilinear subspace learning algorithms. An efficient iterative algorithm is proposed to tackle this essentially integer programming problem. In each step, the matrix structure is refined with a constrained Earth Mover's Distance procedure that incrementally rearranges matrices to become more similar to their low-rank approximations, which have high correlation among features along rows and columns. In addition, we present two extensions of the algorithm for conducting supervised bilinear subspace learning. Experiments in both unsupervised and supervised bilinear subspace learning demonstrate the effectiveness of our proposed algorithms in improving data compression performance and classification accuracy.

Author information

Author/s: Xu, Dong (D); Yan, Shuicheng (S); Lin, Stephen (S); Huang, Thomas S (TS); Chang, Shih-Fu (SF);

Affiliation: Nanyang Technological University, Singapore. dongxu(-atsign-)ntu.edu.sg

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2009-Oct; vol 31 (issue 10) : pp 1913-20

Dates: Created 2009/08/21; Completed 2009/10/06;

PMID: 19696459, status: MEDLINE (last retrieval date: 10/6/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms Artificial Intelligence Data Compression - methods

Data Compression - methods Face - physiology Normal Distribution

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