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Nonnegative Matrix Factorization for Semi-supervised Dimensionality Reduction
[article]
2011
arXiv
pre-print
We show how to incorporate information from labeled examples into nonnegative matrix factorization (NMF), a popular unsupervised learning algorithm for dimensionality reduction. In addition to mapping the data into a space of lower dimensionality, our approach aims to preserve the nonnegative components of the data that are important for classification. We identify these components from the support vectors of large-margin classifiers and derive iterative updates to preserve them in a
arXiv:1112.3714v1
fatcat:aqte53dh5bgebafthhdkrkgaqq