Supervised locally linear embedding

Authors:
Dick de Ridder;Olga Kouropteva;Oleg Okun;Matti Pietikäinen;Robert P. W. Duin
Affiliations:
Department of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands;Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland;Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland;Infotech Oulu and Department of Electrical and Information Engineering, University of Oulu, Finland;Department of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.