Adaptive Low-Rank Kernel Subspace Clustering
This work addresses the challenge of handling non-linear data in subspace clustering, which is incremental as it builds on prior kernel methods by introducing adaptive learning.
The paper tackles the problem of kernel subspace clustering for non-linear models by learning a low-rank kernel matrix, achieving superior results on motion segmentation and image clustering benchmarks compared to existing methods.
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.