A Novel Scalable Algorithm for Supervised Subspace Learning

Abstract

Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly… (More)
DOI: 10.1109/ICDM.2006.7

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