Class-driven nonnegative matrix factorization with manifold regularization for data clustering

Abstract

Nonnegative matrix factorization (NMF) is an effective technique to extract the underlying low-dimensional structure of data by utilizing its parts-based representation, which has been widely used in feature extraction and machine learning. However, NMF is an unsupervised learning algorithm without utilizing the discriminative prior information. In this paper, we put forward a new class-driven NMF with manifold regularization (MCDNMF) algorithm, which incorporates both the local manifold regularization and the label information of data into the NMF model. Specifically, MCDNMF not only encodes the local geometrical structure of data space by using the manifold regularization, but also takes the available label information by introducing the class-driven constraint. This class-driven constraint forces the new representations of data points to be more similar within the same class while different between other classes. Therefore, the discriminative abilities of clustering are greatly boosted. Experimental results on several datasets validate the effectiveness of proposed MCDNMF in comparison with the other state-of-the-art methods.