Nonstationary Distance Metric Learning

TL;DR

This paper introduces COMID-SADL, an adaptive online method for learning distance metrics in nonstationary environments, improving over previous algorithms by effectively tracking changing data structures.

Contribution

The paper presents COMID-SADL, a novel adaptive online algorithm for nonstationary distance metric learning, capable of handling evolving data distributions and constraints.

Findings

COMID-SADL outperforms existing batch and online methods.

It effectively tracks changing metrics in real and synthetic data.

Significant performance improvements are demonstrated.

Abstract

Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we introduce the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes to the feature subspaces in which the class structure is apparent. We propose and evaluate COMID-SADL, an adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We demonstrate COMID-SADL on both real and synthetic data sets and show significant performance improvements relative to previously proposed batch and online distance metric learning algorithms.