关键词: 非线性, 深度自编码器, 自我表示, 多视图子空间聚类

Abstract:

Traditional subspace clustering model has a poor performance in multi-view data and nonlinear data. The paper is devoted to multi-view subspace clustering model based on deep autoencoder so as to improve the learning capability of multi-view space clustering algorithm by introducing the self-expression and weighted sparse representation of subspace clustering to the autoencoder. The target model can cluster data points with complex structure, which proves the effectiveness by multi-view dataset. The result shows that the method has a good performance in mining the multiple clustering structures of data effectively and complementing messages between multiple views. Compared with the existing methods, the performance of this method is greatly improved.

Key words: nonlinearity, deep autoencoder, self-representation, multi-view subspace clustering