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409 Evans Hall Phone (510) 642-2021 Email [email protected] Research Expertise and Interests statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and …
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Welcome I'm Bin Yu, the head of the Yu Group at Berkeley, which consists of 15-20 students and postdocs from Statistics and EECS. I was formally trained as a statistician, …
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
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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 …
four spectral channels. Cloud detection is particularly di–cult in the snow- and ice-covered po- ... Berkeley, CA 94720-3860. Email: [email protected], [email protected] yDepartment of Meteorology, The Pennsylvania State University, University Park, PA 16802. Email: ... angle using four spectral bands …
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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 …
arXiv:0807.3719v1 [stat.ML] 23 Jul 2008 Data Spectroscopy: Eigenspace of Convolution Operators and Clustering Tao Shi∗, Mikhail Belkin† and Bin Yu‡ The Ohio State University∗† and University of California, Berkeley‡ Abstract: This paper focuses on obtaining clustering information in a dis-tribution when iid data are given.
Departments of Statistics and Electrical Engineering and Computer Sciences ... 367 Evans Hall #3860 • Berkeley, CA 94720. phone: 510-642-2781 • fax: 510-642-7892 • [email protected]. Welcome. I'm Bin Yu, the head of the Yu Group at ... UC Berkeley to lead $10M NSF/Simons Foundation program to investigate theoretical underpinnings …
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Verma and Meila [17]. A discussion of some limitations of spectral cluster-ing can be found in Nadler and Galun [7]. A theoretical analysis of statis-tical consistency of different types of spectral clustering is provided in von Luxburg, et al [19]. Similarly to spectral clustering methods, Kernel Principal Component
The canonical nature of spectral clustering also generates interest in variants of the technique. Here, we attempt to better understand the impact of regularized forms of …
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spectral clustering, this provides insight into a graph visualization technique. Our method of studying the eigenvectors of random matrices is original. 1. Introduction. Researchers in …
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In this paper we aim to understand spectral clustering methods based on a population analysis. We concentrate on exploring the connections between the distribution Pand the …
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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 clustering, Diffusion maps and Laplacian eigenmaps.
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insights and heuristics: spectral clustering is a convex relaxation of the Normalized Cut optimization problem (Shi and Malik, 2000), it can identify the connected components in …
The impact of regularization on spectral clustering. Annals of Statistics. 4, 1765 - 1791. C. Lim and B. Yu (2016). Estimation Stability with Cross Validation (ESCV) Journal of …
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 …
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 ...
@WalmartLabs and University of California, Berkeley The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et al. [ Ann. Statist. 41
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