Dimensionality reduction: An interpretation from manifold regularization perspective

Abstract

In this paper, we propose to unify various dimensionality reduction algorithms by interpreting the Manifold Regularization (MR) framework in a new way. Although the MR framework was originally proposed for learning, we utilize it to give a unified treatment for many dimensionality reduction algorithms from linear to nonlinear, supervised to unsupervised… (More)
DOI: 10.1016/j.ins.2014.03.011

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