Adaptive Low-Rank Kernel Subspace Clustering

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.

References in corpus (1)

• Non-linear Dimensionality Regularizer for Solving Inverse Problems

Cited by in corpus (6)

• Self-Supervised Convolutional Subspace Clustering Network
• Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms
• Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
• PMSSC: Parallelizable multi-subset based self-expressive model for subspace clustering
• Matrix Recovery with Implicitly Low-Rank Data
• Scalable Deep $k$-Subspace Clustering