Colored Maximum Variance Unfolding
2008
Conference Paper
ei
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.
Author(s): | Song, L. and Smola, AJ. and Borgwardt, K. and Gretton, A. |
Book Title: | Advances in neural information processing systems 20 |
Journal: | Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 |
Pages: | 1385-1392 |
Year: | 2008 |
Month: | September |
Day: | 0 |
Editors: | Platt, J. C., D. Koller, Y. Singer, S. Roweis |
Publisher: | Curran |
Department(s): | Empirische Inferenz |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007) |
Event Place: | Vancouver, BC, Canada |
Address: | Red Hook, NY, USA |
Digital: | 0 |
ISBN: | 978-1-605-60352-0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{4929, title = {Colored Maximum Variance Unfolding}, author = {Song, L. and Smola, AJ. and Borgwardt, K. and Gretton, A.}, journal = {Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007}, booktitle = {Advances in neural information processing systems 20}, pages = {1385-1392}, editors = {Platt, J. C., D. Koller, Y. Singer, S. Roweis}, publisher = {Curran}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Red Hook, NY, USA}, month = sep, year = {2008}, doi = {}, month_numeric = {9} } |