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Spectral clustering and the high-dimensional Stochastic

Spectral clustering and the high-dimensional Stochastic Block Model Karl Rohe, Sourav Chatterjee and Bin Yu Department of Statistics University of California Berkeley, CA 94720, USA e-mail: [email protected] [email protected] [email protected] Abstract: Networks or graphs can easily represent a diverse set of data sources that

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Statistics at UC Berkeley Department of Statistics

Statistics at UC Berkeley. We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U

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Impact of regularization on spectral clustering

1768 A. JOSEPH AND B. YU Algorithm 1 The RSC-τ Algorithm [2] Input: Laplacian matrix Lτ. Step 1: Compute the n×K eigenvector matrix Vτ. Step 2: Use the K-means algorithm to cluster the rows of Vτ into K clusters. Regularization is introduced in the following way: Let J be a constant matrix with all entries equal to 1/n.Then, in regularized spectral clustering one constructs

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Spectral clustering and the high-dimensional stochastic

spectral clustering has many similarities with the nonlinear dimension reduction or manifold learning techniques such as Diffusion maps and Laplacian eigenmaps [Coifman et al. (2005), Belkin and Niyogi (2003)]. The normalized graph Laplacian L is an essential part of spectral

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Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area Xin Jiang a, Yang Liu b, Bin Yu c, Ming Jiang a a LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China b Harvard School of Public Health, Boston, MA 02215, U.S. c Department of Statistics, University of California, Berkeley

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