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arXiv · 2002.06739

Multiple Flat Projections for Cross-manifold Clustering

Lan Bai·Yuan-Hai Shao·Wei-Jie Chen·Zhen Wang·Nai-Yang Deng
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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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Lan Bai, Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Nai-Yang Deng. 2020-02-17. Multiple Flat Projections for Cross-manifold Clustering. https://doi.org/10.1109/tcyb.2021.3050487

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