Dual graph-regularized Constrained Nonnegative Matrix Factorization for Image Clustering

作者：Sun, Jing; Cai, Xibiao; Sun, Fuming^*; Hong, Richang

来源：KSII Transactions on Internet and Information Systems, 2017, 11(5): 2607-2627.

DOI：10.3837/tiis.2017.05.017

• Nonnegative matrix factorization
• dual graph-regularized
• manifold
• feature manifold
• label information

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• 出版日期2017-5-31
• 单位合肥工业大学; 辽宁工业大学