Adaptive Low-Rank Kernel Subspace Clustering

TL;DR

This paper introduces a novel kernel subspace clustering approach that learns a low-rank kernel matrix to effectively handle non-linear data structures, outperforming existing methods on motion segmentation and image clustering tasks.

Contribution

It proposes a method to learn a low-rank kernel matrix for kernel subspace clustering, enabling better modeling of non-linear data while preserving subspace structures.

Findings

Outperforms standard kernel subspace clustering methods

Achieves superior results on motion segmentation benchmarks

Effective in image clustering tasks

Abstract

In this paper, we present a kernel subspace clustering method that can handle non-linear models. In contrast to recent kernel subspace clustering methods which use predefined kernels, we propose to learn a low-rank kernel matrix, with which mapped data in feature space are not only low-rank but also self-expressive. In this manner, the low-dimensional subspace structures of the (implicitly) mapped data are retained and manifested in the high-dimensional feature space. We evaluate the proposed method extensively on both motion segmentation and image clustering benchmarks, and obtain superior results, outperforming the kernel subspace clustering method that uses standard kernels[Patel 2014] and other state-of-the-art linear subspace clustering methods.