Nonlinear Manifold Learning 6.454 Summary
نویسنده
چکیده
Manifold learning is the process of estimating a low-dimensional structure which underlies a collection of high-dimensional data. Here we review two popular methods for nonlinear dimensionality reduction, locally linear embedding (LLE, [1]) and IsoMap [2]. We also discuss their roots in principal component analysis and multidimensional scaling, and provide a brief comparison of the underlying assumptions, strengths, and weaknesses of each algorithm.
منابع مشابه
Nonlinear Manifold Learning Part II 6.454 Summary
Manifold learning addresses the problem of finding low–dimensional structure within collections of high–dimensional data. Recent interest in this problem was motivated by the development of a pair of algorithms, locally linear embedding (LLE) [6] and isometric feature mapping (IsoMap) [8]. Both methods use local, linear relationships to derive global, nonlinear structure, although their specifi...
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Manifold learning is a dimension reduction method for extracting nonlinear structures of high-dimensional data. Many methods have been introduced for this purpose. Most of these methods usually extract a global manifold for data. However, in many real-world problems, there is not only one global manifold, but also additional information about the objects is shared by a large number of manifolds...
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