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  Colored Maximum Variance Unfolding

Song, L., Smola, A., Borgwardt, K., & Gretton, A. (2008). Colored Maximum Variance Unfolding. In C. Platt, D. Koller, Y. Singer, & S. Roweis (Eds.), Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 (pp. 1385-1392). Red Hook, NY, USA: Curran.

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 Creators:
Song, L, Author
Smola, AJ, Author           
Borgwardt, KM, Author           
Gretton, A1, 2, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while preserving the local distances of the
original data. We show that MVU also optimizes a statistical dependence measure which aims to retain the identity of individual observations under the distancepreserving constraints. This general view allows us to design "colored" variants of MVU, which produce low-dimensional representations for a given task, e.g. subject to class labels or other side information.

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 Dates: 2008-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 4929
 Degree: -

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Title: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2007-12-03 - 2007-12-06

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Title: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Source Genre: Proceedings
 Creator(s):
Platt, C, Editor
Koller, D, Editor
Singer, Y, Editor
Roweis, ST, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1385 - 1392 Identifier: ISBN: 978-1-605-60352-0