PRESERVING LOCAL MANIFOLD IN NOISE DATA CLUSTERING

Abstract

Adaptive subspace clustering techniques, such as LDA Kmeans (henceforth LDAKM), try to perform clustering on a compact discriminative subspace. However, the subspace extracted by LDAKM may be inferior when encountering noise, so it is with the clustering results. In this paper, we generalize the LDAKM algorithm, and propose the Local Manifold Preserving LDAKM (henceforth LMP–LDAKM) approach, which considers both local manifold structure and discriminative information. A Laplacian similarity matrix is introduced in the subspace extraction subprocess for preserving local manifold information while retaining discrimination of latent clusters, and it is also utilized for the weighted Kmeans clustering subprocess in the subspace to eliminate the influence of distant noise data. Experimental results on both benchmark and artificial datasets indicate the effectiveness and noise-robustness of our method.