Spectral Clustering Based on Sparse Representation

Spectral clustering is an efficient clustering algorithm based the information propagation between neighborhood nodes. Its performance is largely dependent on the distance metrics, thus it is possible to boost its performance by adapting more reliable distance metric. Given the advantages of sparse...

Full description

Saved in:
Bibliographic Details
Published inApplied Mechanics and Materials Vol. 556-562; pp. 3822 - 3826
Main Authors Hu, Chen Xiao, Zou, Xian Chun
Format Journal Article
LanguageEnglish
Published Zurich Trans Tech Publications Ltd 01.05.2014
Subjects
Distance Metric
Sparse Representation
Spectral Clustering
Online AccessGet full text

Cover

More Information
Summary:Spectral clustering is an efficient clustering algorithm based the information propagation between neighborhood nodes. Its performance is largely dependent on the distance metrics, thus it is possible to boost its performance by adapting more reliable distance metric. Given the advantages of sparse representation in discriminative ability, robust to noisy and more faithfully to measure the similarity between two samples, we propose an sparse representation algorithm based on sparse representation. The experimental study on several datasets shows that, the proposed algorithm performs better than the sparse clustering algorithms based on other similarity metrics.
Bibliography:Selected, peer reviewed papers from the 2014 International Conference on Mechatronics Engineering and Computing Technology (ICMECT 2014), April 9-10, 2014, Shanghai, China
ISBN:3038351156
9783038351153
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.556-562.3822