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Colored Maximum Variance Unfolding

2008

Conference Paper

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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): Empirical Inference
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: PDF
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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},
  month_numeric = {9}
}