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Manifold clustering is a widely used techniques in pattern recognition and machine learning. It partition a set of input data into several clusters each of which contains data points from a separate, simple low-dimensional manifold. In order to cluster manifold, we propose a novel distance measure based on topology structure that can efficiently represent the underlying manifold. Under this distance...
Manifold clustering aims to partition a set of input data into several clusters each of which contains data points from a separate, simple low-dimensional manifold. This paper presents a novel solution to this problem. The proposed algorithm begins by randomly selecting some neighboring orders of the input data and defining an energy function that is described by geometric features of underlying manifolds...
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