Image Clustering Based on Supervised Graph Regularized Discriminative Concept Factorization

Abstract

Concept Factorization (CF) divides a matrix into the product of three matrices. It is considered as one variant of Non-negative Matrix Factorization (NMF). The biggest difference between the two methods is that CF can be executed in a kernel space. Because of this characteristic, many schemes based on CF have been proposed in computer vision and pattern recognition fields. Recent studies have shown that high dimensional data is often located in a low dimensional manifold space, in order to improve the performance and reduce the storage space, how to find the mapping function is particularly important. In addition, the development of supervised learning methods show that label information is critical to enhance the model’s ability. In this paper, a supervised graph regularized discriminative concept factorization (SGDCF) method is presented for image clustering. In the SGDCF, we make use of local manifold geometry structure and label information. The corresponding multiplicative update solutions and convergence verification are given. Clustering results on four image data sets reveal that the SGDCF outperforms the state-of-the-art algorithms in terms of accuracy and normalized mutual information.