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Multi-view clustering integrates complementary information from multiple views to gain better clustering performance rather than relying on a single view. NMF based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, NMF fails to preserve the locally geometrical structure of the data(More)
Existing multi-view clustering algorithms require that the data is completely or partially mapped between each pair of views. However, this requirement could not be satisfied in most practical settings. In this paper, we tackle the problem of multi-view clustering for unmapped data in the framework of NMF based clustering. With the help of inter-view(More)
Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this(More)
Multi-view clustering has become a hot topic since the past decade and nonnegative matrix factorization (NMF) based multi-view clustering algorithms have shown their superiorities. Nevertheless, two drawbacks prevent NMF based multi-view algorithms from being a better algorithm: (1) The solution of NMF based multi-view algorithms is not unique. (2) Standard(More)
Spectral clustering is widely used in these years. Recently, methods that connect spectral clustering and semi-supervised clustering become popular. These methods improve the result through using constraint information in spectral clustering. Generally, there are two ways to select constrained information, one is random selection method and the other is(More)
Online social networks have become popular platforms for spammers to spread malicious content and links. Existing state-of-the-art optimization methods mainly use one kind of user-generated information (i.e., single view) to learn a classification model for identifying spammers. Due to the diversity and variability of spammers' strategies, spammers'(More)
Sampling is the key aspect for Nyström extension based spectral clustering. Traditional sampling schemes select the set of landmark points on a whole and focus on how to lower the matrix approximation error. However, the matrix approximation error does not have direct impact on the clustering performance. In this article, we propose a sampling(More)
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