关键词: [k]-近邻, 子空间聚类方法, 稀疏, 相似矩阵

Abstract:

In order to obtain a more reasonable affine matrix, a sparse subspace clustering algorithm based on [k]-nearest neighbor and local similarity is proposed. The algorithm first calculates the [k]-nearest neighbor of each point and linearly represents it with [k]-nearest neighbor data points, so that the affine matrix can guarantee a strong local linear relationship in the case of overall sparseness. At the same time, based on the knowledge of graph theory, the affine matrix is constrained by the actual distribution of the data, so that the affine matrix is further reasonably equivalent to the similarity matrix to be spectrally clustered. Experiments are carried out on artificial datasets, randomly generated subspace datasets, image datasets and real datasets. The experimental results show that the algorithm is effective.

Key words: [k]-nearest neighbor, subspace clustering method, sparse, similarity matrix