An Information Retrieval Approach to Finding Dependent Subspaces of Multiple Views

TL;DR

This paper introduces a novel method for finding dependent subspaces of multiple data views, optimized specifically for neighbor retrieval tasks, outperforming traditional CCA in capturing local and nonlinear dependencies.

Contribution

The authors propose a new approach that directly optimizes for cross-view neighborhood similarity, capturing nonlinear and local dependencies better than classical CCA.

Findings

Outperforms alternatives in preserving cross-view neighborhood similarities

Detects nonlinear and local data relationships effectively

Provides insights into local dependencies between views

Abstract

Finding relationships between multiple views of data is essential both for exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variants of Canonical Correlation Analysis (CCA), a classical method seeking correlated components between views. The basic CCA is restricted to maximizing a simple dependency criterion, correlation, measured directly between data coordinates. We introduce a new method that finds dependent subspaces of views directly optimized for the data analysis task of \textit{neighbor retrieval between multiple views}. We optimize mappings for each view such as linear transformations to maximize cross-view similarity between neighborhoods of data samples. The criterion arises directly from the well-defined retrieval task, detects nonlinear and local similarities, is able to measure dependency of data relationships rather than only individual data coordinates, and is related to well understood measures of information retrieval quality. In experiments we show the proposed method outperforms alternatives in preserving cross-view neighborhood similarities, and yields insights into local dependencies between multiple views.