PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Colored Maximum Variance Unfolding
Le Song, Alex Smola, Karsten Borgwardt and Arthur Gretton
In: NIPS 2007, 03 Dec - 06 Dec 2007, Vancouver Canada.

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.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3144
Deposited By:Arthur Gretton
Deposited On:21 December 2007