Adaptive Low-Rank Kernel Subspace Clustering

2017/07/17 by Pan Ji, Ji, Pan, Ian Reid +7
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1707.04974

arxiv created 2019/01/25 · arxiv updated 2019/01/28

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.

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