Empirical Inference
Empirical Inference
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. |
| Links: | |
| 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 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Red Hook, NY, USA |
| Event Name: | Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 978-1-605-60352-0 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTex
@inproceedings{4929,
title = {Colored Maximum Variance Unfolding},
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},
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.},
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},
slug = {4929},
author = {Song, L. and Smola, AJ. and Borgwardt, K. and Gretton, A.},
month_numeric = {9}
}