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Linear algebra
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#	Item
591	Spectral Graph Theory Lecture 15 Iterative solvers for linear equations Daniel A. Spielman Add to Reading List Source URL: www.cs.yale.edu Language: English - Date: 2012-08-24 09:50:05 Numerical linear algebra Matrix theory Linear algebra Eigenvalues and eigenvectors Singular value decomposition Modified Richardson iteration Conjugate gradient method Eigendecomposition of a matrix
592	Latent Outlier Detection and the Low Precision Problem Fei Wang, Sanjay Chawla and Didi Surian University of Sydney Add to Reading List Source URL: www.outlier-analytics.org Language: English - Date: 2013-08-13 08:05:46 Linear algebra Matrix theory Multivariate statistics Robust statistics Non-negative matrix factorization Outlier Singular value decomposition
593	CS168: The Modern Algorithmic Toolbox Lecture #7: Understanding and Using Principal Component Analysis (PCA) Tim Roughgarden & Gregory Valiant∗ April 18, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-06-04 09:49:44 Multivariate statistics Dimension reduction Abstract algebra Linear algebra Analytic geometry Principal component analysis Dimensionality reduction Eigenvalues and eigenvectors Linear regression Euclidean vector Degrees of freedom Eigenface
594	On the Performance Prediction of BLAS-based Tensor Contractions Elmar Peise Diego Fabregat-Traver Add to Reading List Source URL: www.dcs.warwick.ac.uk Language: English - Date: 2014-11-22 16:55:21 Numerical linear algebra Numerical software Tensors Basic Linear Algebra Subprograms Tensor contraction
595	Use of Semi-separable Approximate Factorization and Direction-preserving for Constructing Effective Preconditioners X. Sherry Li Lawrence Berkeley National Laboratory Ming Gu (UC Berkeley) Add to Reading List Source URL: crd-legacy.lbl.gov Language: English - Date: 2010-03-18 15:12:23 Numerical linear algebra Matrices Linear algebra Matrix theory Operator theory Cholesky decomposition Singular value decomposition Matrix Schur complement Triangular matrix Rank Hierarchical matrix
596	http://www.r-project.org/ A (very) short introduction to R and do the following (assuming you work on a Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-03-18 06:24:32 Linear algebra Array programming languages Vectors Functional languages Vector calculus R Euclidean vector Matrix Vector space Norm Vector Command-line interface
597	Efficiency of general Krylov methods on GPUs – An experimental study Hartwig Anzt, Jack Dongarra University of Tennessee Knoxville, TN, USA {hanzt,dongarra}@icl.utk.edu Add to Reading List Source URL: icl.cs.utk.edu Language: English - Date: 2016-04-04 12:47:33 Numerical linear algebra Krylov subspace Generalized minimal residual method Iterative method Biconjugate gradient stabilized method Preconditioner Matrix Lis Sparse matrix Conjugate gradient method IML++ Arnoldi iteration
598	559 Do . Math. J. DMV Metri s on State Spa es This arti le is dedi ated to Ri hard V. Kadison Add to Reading List Source URL: www.math.uiuc.edu Language: English - Date: 2014-07-15 07:20:51 Linear algebra Norm Weak topology Topological vector spaces
599	ORDINARY LEAST SQUARES REGRESSION MATH 196, SECTION 57 (VIPUL NAIK) Corresponding material in the book: For the computational process, Section 5.3 of the book is relevant. For comparing notation: • Add to Reading List Source URL: files.vipulnaik.com Language: English - Date: 2016-08-13 11:33:29 Regression analysis Estimation theory Parametric statistics Econometrics Least squares Linear regression Ordinary least squares Linear algebra Matrix Vector space Line Linear least squares
600	DSP: Robust Semi-Supervised Dimensionality Reduction using Dual Subspace Projections Su Yan∗ Sofien Bouaziz Dongwon Lee IBM Almaden Research Center Ecole Polytechnique F´ed´erale de Lausanne Pennsylvania State Univer Add to Reading List Source URL: sofienbouaziz.com Language: English - Date: 2015-11-19 01:50:34 Multivariate statistics Dimension reduction Geostatistics Functional analysis Linear algebra Dimensionality reduction Principal component analysis Nonlinear dimensionality reduction K-nearest neighbors algorithm Cluster analysis Kernel method Linear discriminant analysis

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