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1 Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes Dong Yin, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran Abstract In this paper, we consider…
Introduction to H2-matrices Steffen Börm Christian-Albrechts-Universität, Kiel Winterschool on Hierarchical Matrices S. Börm CAU Kiel H2-matrices WinterschoolH-Matrices…
Sparse recovery for spherical harmonic expansionsRachel Ward1 Workshop Sparsity and Cosmology, Nice May 31, 2011 Cosmic Microwave Background Radiation (CMB) map • Temperature
SCATTERING MATRICES AND WEYL FUNCTIONS JUSSI BEHRNDT, MARK M. MALAMUD and HAGEN NEIDHARDT Abstract For a scattering system {AΘ, A0} consisting of selfadjoint extensions
07a - 14.6 Notes - Transformation Matrices14-6: Transformation Matrices Unit 1 - Matrices Learning Targets: • Find the images of points under different types of transformations
Forcing with matrices of countable elementary submodels Borǐsa Kuzeljević IMS-JSPS Joint Workshop - IMS Singapore, January 2016 Borǐsa Kuzeljević (NUS) Matrices of…
Approximation Bounds for Sparse Principal Component Analysis Alexandre d’Aspremont CNRS Ecole Polytechnique With Francis Bach INRIA-ENS and Laurent El Ghaoui UC Berkeley…
Network Analysis with matrices For us a Network is an undirected, unweighted graph G with N nodes. Usually represented through a symmetric adjacency matrix A ∈ RN×N
Mingli Chen † Kengo Kato ‡ Chenlei Leng § Abstract Data in the form of networks are increasingly available in a variety of areas, yet statistical models
ORFE, Princeton University & EECS, U.C. Berkeley Available online at www.princeton.edu/∼aspremon 1 • We estimate a sample covariance matrix Σ from empirical
QIYU SUN AND MICHAEL UNSER Abstract. The fractional Laplacian (−) γ/2 commutes with the primary coordination transformations in the Euclidean space Rd : dilation,
Eigenvalue Algorithms for Symmetric Hierarchical MatricesThomas Mach submitted to Department of Mathematics at Chemnitz University of Technology in accordance with the requirements
x y i Reminder: Centering Data α = y− βx y1 y2 ... Correlation vs. Regression Slope = cos θ Regression Slope: y x R2 Statistic R2 = explained variance
New explicit constructions of RIP matricesJean Bourgain1 Steven J. Dilworth2 Kevin Ford3 Sergei Konyagin4 Denka Kutzarova5 3University of Illinois 4Steklov Mathematical Institute
Integer Matrices with Constrained Eigenvalues - Cyclotomic matrices and charged signed graphsGraeme Taylor A question Which integer symmetric matrices have all eigenvalues
Iain Johnstone, Statistics, Stanford [email protected] SEA’06@MIT – p.1 • Hypothesis Testing: Single and Double Wishart • Eigenvalue densities •
Ron Goldman Department of Computer Science Rice University Affine Transformation Matrices € € € € € € L = z–Axis • € Matrices
MARKOV CHAINS WITH RANDOM TRANSITION MATRICES BY YUKIO TAKAHASHI Introduction. Let Pι (t=l, 2, 3, •••) be the transition matrix from epoch t—1 to
Coloring sparse random k-colorable graphs in polynomial expected time Julia Böttcher, TU München, Germany MFCS, Gdansk, 2005 -------------- Definitions V (G) = {1, . .…
1 Dynamic Sparse Factor Analysis Veronika Ročková Joint work with Ken McAlinn and Enakshi Saha December 19th, 2018 Vienna University of Economics and Business 1 65 1 Sparse…