An Improved Subspace Clustering Algorithm Based on Sparse Representation

Abstract

Subspace clustering is an effective algorithm refer to the problem which separate the data lying on a union of subspaces. Usually the algorithm consists of two steps. First, an affinity matrix is calculated from the self-representation of the data. Second, spectral clustering is used to cluster the data by the affinity matrix. The paper introduces a new method based on the sparse subspace clustering. The new method used a new objective which considered both the sparseness and grouping effect. And the affinity matrix which is calculated from the self-representation of the data is furthered optimized. It proved to be more efficient than existing subspace clustering methods.