Spectral Clustering Based on Sparse Representation

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 3822-3826
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.3822
• Online since: May 2014
• Authors: Chen Xiao Hu*, Xian Chun Zou
• Keywords: Distance Metric, Sparse Representation, Spectral Clustering

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