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 extract...
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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 -drivenconstraint. This class -drivenconstraint 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.
Recently, concept factorization (CF), which is a variant of nonnegative matrix factorization, has attracted great attentions in image representation. In CF, each concept is modeled as a nonnegative linear combination ...
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Recently, concept factorization (CF), which is a variant of nonnegative matrix factorization, has attracted great attentions in image representation. In CF, each concept is modeled as a nonnegative linear combination of the data points, and each data point as a linear combination of the concepts. CF has impressive performances in data representation. However, it is an unsupervised learning method without considering the label information of the data points. In this paper, we propose a novel semi supervised CF method, called class-driven concept factorization (CDCF), which associates the class labels of data points with their representations by introducing a class-driven constraint. This constraint forces the representations of data points to be more similar within the same class while different between classes. Thus, the discriminative abilities of the representations are enhanced in the image representation. Experimental results on several databases have shown the effectiveness of our proposed method in terms of clustering accuracy and mutual information. (C) 2016 Elsevier B.V. All rights reserved.
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