关键词: 子空间, 聚类, 稀疏表示, [k]近邻, 人脸聚类

Abstract: Sparse subspace clustering is a newly developed clustering framework for high-dimensional data. Since actual data do not completely satisfy the subspace model assumption, a novel sparse subspace clustering with [k] nearest neighbor constraint is proposed. The proposed algorithm combines the subspace structure, [k] nearest neighbor and the distance information and adds [k] nearest neighbor constraint term into the sparse subspace model. The added term corresponds the intuitive knowledge that closer samples have large similarity coefficients and do not change the sparsity of coefficient matrix. The experimental result on face databases Extended YaleB, ORL, AR, object image database COIL and a handwritten digits database USPS shows that the proposed algorithm has competitive performance.

Key words: subspace, clustering, sparse representation, [k] nearest neighbors, face clustering