Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Machine Learning
2019-07-26 v1 Information Theory
math.IT
Machine Learning
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.
Keywords
Cite
@article{arxiv.1907.10906,
title = {Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph},
author = {Gen Li and Yuantao Gu},
journal= {arXiv preprint arXiv:1907.10906},
year = {2019}
}
Comments
22 pages, 5 figures