Spectral Clustering Based on Sparse Representation
Hu Chen, Xian Chun Zou · Applied Mechanics and Materials · 2014
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.