Colored Maximum Variance Unfolding
Abstract
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximiz- ing 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 distance- preserving 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.
Cite
Text
Song et al. "Colored Maximum Variance Unfolding." Neural Information Processing Systems, 2007.Markdown
[Song et al. "Colored Maximum Variance Unfolding." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/song2007neurips-colored/)BibTeX
@inproceedings{song2007neurips-colored,
title = {{Colored Maximum Variance Unfolding}},
author = {Song, Le and Gretton, Arthur and Borgwardt, Karsten and Smola, Alex J.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {1385-1392},
url = {https://mlanthology.org/neurips/2007/song2007neurips-colored/}
}