MANIFOLD LEARNING, A PROMISED LAND OR WORK IN PROGRESS? (FriAmOR5)

Author(s) : Mei-Chen Yeh (University of California Santa Barbara, United States of America) I-Hsiang Lee (University of California Santa Barbara, United States of America) Gang Wu (University of California Santa Barbara, United States of America) Yi Wu (University of California Santa Barbara, United States of America) Edward Chang (University of California Santa Barbara, United States of America)

Abstract : Tasks of image clustering and classification often deal with data of very high dimensions. To alleviate the dimensionality curse, several methods, such as Isomap, LLE and KPCA, have recently been proposed and applied to learn low-dimensional non-linear embedded manifolds in high-dimensional spaces. Unfortunately, the scenarios in which these methods appear to be effective are very contrived. In this work, we empirically examine these methods on a realistic but not-so-difficult dataset. We discuss the promises and limitations of these dimension-reduction schemes

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