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A unified framework for high-dimensional analysis of

Department of EECS1 Department of CS2 Department of Statistics3 UC Berkeley UT Austin UC Berkeley October 2010 Abstract High-dimensional statistical inference deals with models in which the the number of parame-ters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain

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, …

Spectral clustering and the high-dimensional Stochastic …

Spectral clustering is a popular and computationally feasible method to discover these communities. The Stochastic Block Model (Holland et al., 1983) is a social network model with …

ICML_final_08.dvi

closely related to non-parametric spectral methods, such as spectral clustering (e.g., [8]) and Kernel Prin-cipal Components Analysis [11]. Those methods, as well as certain methods in manifold learning (e.g., [1]), construct a kernel matrix or a graph Laplacian ma-trix associated to a data set. The eigenvectors and

Retrieval Using MISR Data A Hierarchical Bayesian …

Yueqing Wang, Department of Statistics, University of California at Berke-ley, CA 94720-3860 (E-mail: [email protected]). Xin Jiang, LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China. Bin Yu, Department of Electrical Engineering and Computer Sciences, Uni-versity of California at Berkeley, CA 94720-3860.

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, U

High-dimensionalcovariance estimation by …

mentwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o(√ s). In our second result, we show that with probability converging to one, the estimate bΘ correctly specifies the zero pattern of the concentration

jasa_resub.dvi

Tao Shi is Assistant Professor, Department of Statistics, the Ohio State University, Columbus, OH 43210 (Email: [email protected]). Bin Yu is Professor, Department of Statistics, University of California, Berkeley, CA 94720 (Email: [email protected]). Eugene Clothiaux is Associate Professor, Department of Mete-

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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Daytime Arctic Cloud Detection based on

Tao Shi is Assistant Professor, Department of Statistics, the Ohio State University, Columbus, OH 43210 (Email: [email protected]). Bin Yu is Professor, Department of Statistics, University of California, Berkeley, CA 94720 (Email: [email protected]). Eugene Clothiaux is Associate Professor, Department of Mete-

Statistics at UC Berkeley | Department of Statistics

Subject: Image Created Date: 6/20/2008 11:36:43 AM

Minimax rates of estimation for high-dimensional …

[email protected] [email protected] Bin Yu1,2 [email protected] Departments of Statistics1, and EECS2 UC Berkeley, Berkeley, CA 94720 Statistics Technical Report October 1, 2009 Abstract Consider the standardlinear regressionmodelY = Xβ∗+w, where Y ∈ Rn is an observation

Spectral clustering and the high-dimensional stochastic …

spectral clustering algorithm. For image segmentation, Shi and Malik (2000) sug-gested spectral clustering on an inferred network where the nodes are the pixels and the edges are determined by some measure of pixel similarity. In this way, spectral clustering has many similarities with the nonlinear dimension reduction

Stagewise Lasso

Peng Zhao [email protected] Department of Statistics University of Berkeley 367 Evans Hall Berkeley, CA 94720-3860, USA Bin Yu [email protected] Department of Statistics University of Berkeley 367 Evans Hall Berkeley, CA 94720-3860, USA Editor: Saharon Rosset Abstract

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stat berkeley edu binyu ps spectral Zenith 791

Department of Computer Science and Engineering, Ohio State University Bin Yu [email protected] Department of Statistics, University of California Berkeley Abstract In this paper we develop a …

Bin Yu

I'm Bin Yu, the head of the Yu Group at Berkeley, which consists of 12-15 students and postdocs from Statistics and EECS. I was formally trained as a statistician, but my research interests …

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Impact of regularization on spectral clustering

Here, we attempt to better understand the impact of regularized forms of spectral clustering for community detection in networks. In particular, we focus on the regularized spectral clustering …

High-dimensionalcovariance estimation by minimizing

mentwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o(√ s). In our second result, we show that with probability converging to one, the estimate bΘ correctly specifies the zero pattern of the concentration

High-dimensionalcovariance estimation by minimizing

The area of high-dimensional statistics deals with estimation in the "large p, small n" setting, where p and n corre-spond, respectively, to the dimensionality of the data and the sample …

High-dimensionalcovarianceestimation byminimizing ℓ …

Berkeley, CA 94720-1776 USA e-mail: [email protected] [email protected] [email protected] [email protected] Abstract: Given i.i.d. observations of a random vector X ∈ Rp, we study the problem of estimating both its covariance matrix Σ∗, and its inverse covariance or concentration matrix Θ ∗= (Σ )−1 ...

High-dimensionalcovarianceestimation byminimizing ℓ

Berkeley, CA 94720-1776 USA e-mail: [email protected] [email protected] [email protected] [email protected] Abstract: Given i.i.d. observations of a random vector X ∈ Rp, we study the problem of estimating both its covariance matrix Σ∗, and its inverse covariance or concentration matrix Θ ∗= (Σ )−1 ...