An Integer Programming Approach To Subspace Clustering With Missing Data

TL;DR

This paper introduces an integer programming method for subspace clustering with missing data, effectively handling high-rank data, high missing data percentages, and similar subspaces, outperforming existing algorithms.

Contribution

The paper presents a novel integer programming framework with column-generation and Benders decomposition for improved subspace clustering with missing data.

Findings

Achieves better clustering accuracy on high-rank data

Performs well with high missing data percentages

Outperforms state-of-the-art methods in challenging scenarios

Abstract

In the Subspace Clustering with Missing Data (SCMD) problem, we are given a collection of n partially observed d-dimensional vectors. The data points are assumed to be concentrated near a union of low-dimensional subspaces. The goal of SCMD is to cluster the vectors according to their subspace membership and recover the underlying basis, which can then be used to infer their missing entries. State-of-the-art algorithms for SCMD can fail on instances with a high proportion of missing data, full-rank data, or if the underlying subspaces are similar to each other. We propose a novel integer programming approach for SCMD. The approach is based on dynamically determining a set of candidate subspaces and optimally assigning points to selected subspaces. The problem structure is identical to the classical facility-location problem, with subspaces playing the role of facilities and data points that of customers. We propose a column-generation approach for identifying candidate subspaces combined with a Benders decomposition approach for solving the linear programming relaxation of the formulation. An empirical study demonstrates that the proposed approach can achieve better clustering accuracy than state-of-the-art methods when the data is high-rank, the percentage of missing data is high, or the subspaces are similar.