Xiaoyan Wang, Caixia Zhang, Yanping Bai
subspace clustering, spectral clustering, k-means, SOM
Sparse Subspace Clustering constructs a sparse similarity graph by using the coefficient of sparse representation to subspace clustering. Based on sparse representation techniques, the algorithm gets the sparse coefficient by using l1-minimization and gets clusters’ data by spectral clustering algorithm. The spectral clustering algorithm depends on k-means algorithm for data clustering, while k-means algorithm is sensitive to the choice of initial starting conditions and it needs iterations. In order to avoid the drawbacks of k-means algorithm, we propose two modified Sparse Subspace Clustering algorithm, then the results are not be affected by the centers or the iterations. In one of the method, we get the clusters by comparing the positions of nonzero elements in the sparse adjacent matrix of similarity graph and the eigenvector. And in the second method, we use SOM algorithm instead of k-means algorithm. The experiment results show our proposed algorithm outperforms the initial Sparse Subspace Clustering.
Cite this paper
Xiaoyan Wang, Caixia Zhang, Yanping Bai. (2016) Two Modified Sparse Subspace Clustering. International Journal of Mathematical and Computational Methods, 1, 400-404