关键词: 子空间聚类, 低秩性, 稀疏性, 亲和度矩阵

Abstract: Based on the low rank representation algorithm, this paper proposes a new model. The new model constructs an affinity graph that reveals the intrinsic relation of the data to complete the clustering task. First of all, this algorithm reproduces the data dictionary by using matrix decomposition principle of the original data, this work can filter out part of the noise. Secondly, the algorithm uses the sparsity of data to strengthen local constraints, and construct nonnegative low rank affinity graphs for given data vectors. The weight of the edge in the affinity graph is obtained from the non-negative low rank sparse coefficient matrix, which is constructed by each data sample as a linear combination of other data samples. The affinity graph thus obtained can show the subspace structure of the data, but also the performance of local linear structure. Finally, the results show that compared with the existing subspace algorithm, the clustering effect of the non-negative local constrained low rank subspace algorithm proposed in this paper is improved obviously.

Key words: subspace clustering, low rank, sparseness, affinity matrix