Multiple Flat Projections for Cross-manifold Clustering

• URL: http://arxiv.org/abs/2002.06739v1
• Date: Mon, 17 Feb 2020 02:16:00 GMT
• Title: Multiple Flat Projections for Cross-manifold Clustering
• Authors: Lan Bai, Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Nai-Yang Deng
• Abstract summary: Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of cross-manifold structures. We propose a Multiple Flat Projections Clustering (C) to deal with cross-manifold clustering problems.
• Score: 11.616653147570446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods.

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