Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data

TL;DR

This paper enhances the Shape Interaction Matrix for subspace clustering, making it robust to noise and missing data, and demonstrates its effectiveness on challenging real-world datasets.

Contribution

We propose a robustification of the SIM that handles corrupted and incomplete data, extending its applicability to practical subspace clustering tasks.

Findings

Outperforms state-of-the-art methods on motion segmentation tasks.

Effectively handles corrupted and missing data in clustering.

Provides a simple, efficient algorithm with theoretical justification.

Abstract

The Shape Interaction Matrix (SIM) is one of the earliest approaches to performing subspace clustering (i.e., separating points drawn from a union of subspaces). In this paper, we revisit the SIM and reveal its connections to several recent subspace clustering methods. Our analysis lets us derive a simple, yet effective algorithm to robustify the SIM and make it applicable to realistic scenarios where the data is corrupted by noise. We justify our method by intuitive examples and the matrix perturbation theory. We then show how this approach can be extended to handle missing data, thus yielding an efficient and general subspace clustering algorithm. We demonstrate the benefits of our approach over state-of-the-art subspace clustering methods on several challenging motion segmentation and face clustering problems, where the data includes corrupted and missing measurements.