Scalable variable selection for two-view learning tasks with projection operators

TL;DR

This paper introduces a scalable variable selection method for two-view learning that leverages projection operators and kernel functions to handle large datasets and nonlinear correlations.

Contribution

The novel method efficiently performs variable selection in large-scale two-view problems using projection operators and kernel-based correlation measures.

Findings

Method scales to millions of samples

Effective in selecting relevant features

Validated on synthetic and real datasets

Abstract

In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where number of data samples could be even millions. In a nutshell, our method performs variable selection by iteratively selecting variables that are highly correlated with the output variables, but which are not correlated with the previously chosen variables. To measure the correlation, our method uses the concept of projection operators and their algebra. With the projection operators the relationship, correlation, between sets of input and output variables can also be expressed by kernel functions, thus nonlinear correlation models can be exploited as well. We experimentally validate our approach, showing on both synthetic and real data its scalability and the relevance of the selected features. Keywords: Supervised variable selection, vector-valued learning, projection-valued measure, reproducing kernel Hilbert space