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Isometric Correction for Manifold Learning

TitleIsometric Correction for Manifold Learning
Publication TypeMiscellaneous
Year of Publication2010
AuthorsBehmardi, B., and R. Raich
Date Published11/2010
Keywordsisometric correction, isometric embedding, manifold learning
Abstract

In this paper, we present a method for isometric correction of manifold learning techniques. We first present an isometric nonlinear dimension reduction method. Our proposed method overcomes the issues associated with well-known isometric embedding techniques such as ISOMAP and maximum variance unfolding (MVU), i.e., computational complexity and the geodesic convexity requirement. Based on the proposed algorithm, we derive our isometric correction method. Our approach follows an isometric solution to the problem of local tangent space alignment. We provide a derivation of a fast iterative solution. The performance of our algorithm is illustrated on both synthetic and real datasets compared to other methods.

URLhttp://www.aaai.org/ocs/index.php/FSS/FSS10/paper/view/2309