Colored Maximum Variance Unfolding

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.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton
• Keywords: Information Technology, Low-dimensional Representations, Maximum Variance Unfolding, NIPS 2007, Statistical Dependence Measure