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Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
[article]
2016
arXiv
pre-print
Spectral methods are popular in detecting global structures in the given data that can be represented as a matrix. However when the data matrix is sparse or noisy, classic spectral methods usually fail to work, due to localization of eigenvectors (or singular vectors) induced by the sparsity or noise. In this work, we propose a general method to solve the localization problem by learning a regularization matrix from the localized eigenvectors. Using matrix perturbation analysis, we demonstrate
arXiv:1609.02906v1
fatcat:cwkhymgfcfb2nawo2c22a6oxui