Comparative Study of Nonlinear Methods for Manifold Learning

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Bernstein A., Burnaev E., Erofeev P.


Proc. of the conf. "Information Technologies and Systems". 2012. P. 85–91.


In this paper manifold embedding and reconstruction procedures are considered in the scope of unsupervised dimension reduction problem. Standard approaches (Isomap, LLE, LTSA, etc.) are compared to newly proposed Grassman-Stiefel Eigenmaps (GSE) algorithm. It turned out that GSE provides best manifold reconstruction abilities on test problems.

Keywords: Data Analysis, Dimension Reduction


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