Bin Yu | Department of Statistics
409 Evans Hall Phone (510) 642-2021 Email [email protected] Research Expertise and Interests statistical inference for high dimensional data and interdisciplinary research …
Spectral clustering and the high-dimensional Stochastic …
[email protected] Abstract: Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people ... Spectral clustering is a popular and computationally feasible method to discover these communities. The Stochastic Block Model (Holland et al ...
Editorial Manager(tm) for Remote Sensing of …
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 …
Spectral clustering and the high-dimensional stochastic …
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 …
Bin Yu
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, …
Research
Current research topics of my group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), analysis and methods for spectral clustering for undirected and directed graphs; and our data problems come from diverse interdisciplinary areas including genomics, neuroscience, remote sensing, document ...
MULTIPLE-KERNEL LEARNING-BASED UNMIXING …
d Department of Statistics, University of California, Berkeley, CA 94720-3860, United States ABSTRACT Detection of clouds in satellite-generated radiance images, including those from MODIS, is an important first step in many applications of these data. In this paper we apply spectral unmixing to this problem with the aim of estimating
Cloud Detection over Snow and Ice Using MISR Data
four spectral channels. Cloud detection is particularly di–cult in the snow- and ice-covered po-lar regions and availability of the novel MISR angle-dependent radiances motivates …
Artificialintelligenceandstatistics
puter science, statistics/machine learning, psychol-ogy, neuroscience, materials science, mechanical en-gineering, and computer hardware design. Excite-ment surrounding AI is now exploding. Ideas drawn from the field form the core of both start-ups and academic divisions, and new developments are be-ing reported throughout the …
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 …
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 clustering, Diffusion maps and Laplacian eigenmaps.
A Hierarchical Bayesian Approach for Aerosol …
89 particles in the atmosphere justify the spatial smoothness of AOD from a physical viewpoint. To flexibly 90 describe various aerosol conditions, our model regards AOD values and mixing vectors as continuous pa- 91 rameters. This expands the set of possible compositions beyond the 74 pre-fixed choices of MISR. We show 92 how this enriched …
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By Karl Rohe, Sourav Chatterjee and Bin Yu University of …
spectral clustering, the di erence is immaterial because both de nitions have the same eigenvectors. The spectral clustering algorithm addressed in this paper is de ned as …
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 …
Data spectroscopy: Eigenspaces of convolution …
the intuitions underlying existing spectral techniques such as spectral clustering and Kernel Principal Components Analysis, and provide new understanding into their usability and modes of failure. Simu-lation studies and experiments on real-world data are conducted to show the potential of our algorithm. In particular, DaSpec is found
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Contribute to lqdid/id development by creating an account on GitHub.
Arti cial Intelligence and Statistics
graduate data science course at Berkeley.5 A population (P) re ects the conditions under which observations were generated and forms the foundation of sampling inference. Understanding P helps one recognize randomness in the data generating process, and hence the uncertainty (or er-rors) in a data result.
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Stat Berkeley Edu Binyu Ps Spectral Zenith Pdf. stat berkeley edu binyu ps spectral zenith 791 pdf. Berkeley edu binyu ps spectral zenith pdf google search,Our company is a largescale heavy enterprise that taking heavy mining machinery manufactory as main products and integrated with scientific research, production, and marketing.
Detection of Daytime Arctic Clouds using MISR and …
Amongst the 36 spectral radiances available on the Moderate Resolution Imag-ing Spectroradiometer (MODIS) seven of them are used operationally for detection of ... ‡Department of Statistics, University of California, Berkeley, CA 94720-3860. Email: [email protected]
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 …
Spectral clustering and the high-dimensional Stochastic …
Aside from the implication for spectral clustering, this provides insight into a graph visualization technique. Our method of studying the eigenvectors of random matrices is …
A path following algorithm for Sparse Pseudo-Likelihood …
that the approximation is better the sparser the precision matrix. In what follows, X is a Rn×p matrix containing in each of its n rows observations of the zero- mean random vector X with covariance matrixΣ. Denote by Xj the j-th entry of X and by XJ∗ the (p − 1) dimensional vector resulting from deleting Xj from X.For a given j, we can permute the order of the …
Combined Features and Kernel Design for Noise Robust …
partment of Statistics∗ at University of California, Berkeley ... @kcl.ac.uk, [email protected]). Finan-cial support from EPSRC under grant EP/D053005/1 is gratefully acknowl-edged. Bin Yu thanks the National Science Foundation for grants NSF SES-0835531 (CDI) and NSFC (60628102). aim to reduce explicitly the effects of noise …
University of California, Berkeley
Created Date: 2/16/2010 1:33:21 PM
Impact of regularization on spectral clustering
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 …
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
Data Spectroscopy: Eigenspaces of Convolution …
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
Data Spectroscopy: Learning Mixture Models using …
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 spectral frame-work for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often used for the
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