Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
arXiv:1907.10906
Abstract
Spectral Method is a commonly used scheme to cluster data points lying close to Union of Subspaces by first constructing a Random Geometry Graph, called Subspace Clustering. This paper establishes a theory to analyze this method. Based on this theory, we demonstrate the efficiency of Subspace Clustering in fairly broad conditions. The insights and analysis techniques developed in this paper might also have implications for other random graph problems. Numerical experiments demonstrate the effectiveness of our theoretical study.
arXiv comment22 pages, 5 figures
References in corpus (5)
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- Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces
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