Multilinear Subspace Clustering

TL;DR

This paper introduces a novel multilinear subspace clustering algorithm for unsupervised image data segmentation, demonstrating competitive accuracy and improved computational efficiency over existing methods.

Contribution

The paper proposes the Multilinear Subspace Clustering (MSC) algorithm tailored for 2-D data, extending the union of subspaces model to multilinear structures.

Findings

MSC achieves high clustering accuracy on YaleB and Olivetti datasets.

MSC offers better computational efficiency compared to existing UOS-based algorithms.

The model effectively captures multilinear data structures for improved segmentation.

Abstract

In this paper we present a new model and an algorithm for unsupervised clustering of 2-D data such as images. We assume that the data comes from a union of multilinear subspaces (UOMS) model, which is a specific structured case of the much studied union of subspaces (UOS) model. For segmentation under this model, we develop Multilinear Subspace Clustering (MSC) algorithm and evaluate its performance on the YaleB and Olivietti image data sets. We show that MSC is highly competitive with existing algorithms employing the UOS model in terms of clustering performance while enjoying improvement in computational complexity.