Optimal Solutions for Sparse Principal Component Analysis

TL;DR

This paper introduces a new semidefinite relaxation and a greedy algorithm for sparse principal component analysis, enabling efficient computation of solutions with guaranteed optimality conditions in various applications.

Contribution

It presents a novel semidefinite relaxation and a greedy algorithm for sparse PCA, along with conditions for global optimality, applicable to subset selection and sparse recovery.

Findings

Algorithm computes solutions with O(n^3) complexity.

Provides conditions for global optimality test in O(n^3).

Achieves globally optimal solutions in many artificial and biological data cases.

Abstract

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. We formulate a new semidefinite relaxation to this problem and derive a greedy algorithm that computes a full set of good solutions for all target numbers of non zero coefficients, with total complexity O(n^3), where n is the number of variables. We then use the same relaxation to derive sufficient conditions for global optimality of a solution, which can be tested in O(n^3) per pattern. We discuss applications in subset selection and sparse recovery and show on artificial examples and biological data that our algorithm does provide globally optimal solutions in many cases.