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Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
Concept Factorization (CF) is a modified version of Nonnegative Matrix Factorization (NMF) and both of them have been proved to be effective matrix factorization methods for dimensionality reduction and data clustering. However, CF is essentially an unsupervised method which cannot utilize any prior knowledge of data. In this paper, we propose a new semi-supervised concept factorization method, called...
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