关键词: 子空间聚类, 隐子空间聚类（LSC）, 判别式字典训练, 增量式字典训练

Abstract: Compared with the traditional subspace clustering algorithms, the latent subspace clustering (LSC) algorithm based on sparse representation has faster clustering speed, thereby it can be applied into larger data sets. However it still has two shortcomings. One is the randomness and slowness in dictionary training phase and the other is its occupying too much memory. On the basis of the LC-KSVD algorithm, this paper proposes the ILSC (improved LSC) algorithm to obtain its enhanced clustering accuracy by adding some labels into the dictionary training phase to improve its discrimination. However, compared with the LSC algorithm, ILSC algorithm consumes more time in dictionary training phase. In order to circumvent this drawback, based on the idea of incremental training, this paper develops the I2LSC (incremental ILSC) algorithm to achieve comparable clustering performance to ILSC algorithm in the sense of clustering accuracy, NMI (normalized mutual information) and RI (Rand index), but faster speed than ILSC.

Key words: subspace clustering, latent subspace clustering (LSC), discriminant dictionary training, incremental dictionary training