Principal value - PDFSEARCH.IO - Document Search Engine

Principal value
Results: 874

#	Item
261	DO SEMIDEFINITE RELAXATIONS REALLY SOLVE SPARSE PCA? arXiv:1306.3690v1 [math.ST] 16 JunBy Robert Krauthgamer Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-06-17 20:40:45 Data analysis Singular value decomposition Matrices Covariance and correlation Linear algebra Principal component analysis Multivariate normal distribution Covariance matrix Eigenvalues and eigenvectors Statistics Algebra Mathematics
262	CUR from a Sparse Optimization Viewpoint Jacob Bien∗ Department of Statistics Stanford University Stanford, CA 94305 Add to Reading List Source URL: www.cs.yale.edu Language: English - Date: 2010-10-28 23:49:43 Numerical linear algebra Mathematical optimization Multivariate statistics Matrix theory Principal component analysis Sparse approximation Sparse matrix Singular value decomposition Matrix Algebra Mathematics Linear algebra
263	Statistical Inference and Learning- Ex-4 Jonathan Rosenblatt and Boaz Nadler due Feb. 15, 2015 Q1 A continuous time Martingale is a stochastic process defined as follows. Let X(t) be a sequence of random variables with a Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2015-01-29 08:44:15 Covariance and correlation Data analysis Singular value decomposition Matrices Probability theory Covariance Principal component analysis Martingale Eigenvalues and eigenvectors Statistics Algebra Mathematics
264	Principal Components Analysis - SPSS In principal components analysis (PCA) and factor analysis (FA) one wishes to extract from a set of p variables a reduced set of m components or factors that accounts for most of th Add to Reading List Source URL: core.ecu.edu Language: English - Date: 2014-12-10 19:44:31 Data analysis Covariance and correlation Singular value decomposition Factor analysis Principal component analysis Varimax rotation Eigenvalues and eigenvectors Variance Linear regression Statistics Multivariate statistics Regression analysis
265	A Tutorial on Principal Component Analysis Jonathon Shlens∗ Google Research Mountain View, CA 94043 Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-04-06 20:15:01 Matrices Matrix theory Singular value decomposition Multivariate statistics Principal component analysis Eigenvalues and eigenvectors Covariance matrix Kernel principal component analysis Diagonalizable matrix Algebra Linear algebra Mathematics
266	1 Learning Graphs from Signal Observations under Smoothness Prior arXiv:1406.7842v1 [cs.LG] 30 Jun 2014 Add to Reading List Source URL: web.media.mit.edu Language: English - Date: 2015-03-29 18:21:36 Singular value decomposition Graphical models Multivariate statistics Probability theory Graph theory Topology Eigenvalues and eigenvectors Principal component analysis Graph Mathematics Algebra Statistics
267	Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits Omar Choudary and Markus G. Kuhn Computer Laboratory, University of Cambridge Add to Reading List Source URL: eprint.iacr.org Language: English - Date: 2014-10-27 10:40:36 Multivariate statistics Singular value decomposition Data analysis Matrix theory Principal component analysis Linear discriminant analysis Eigenvalues and eigenvectors Multivariate normal distribution Covariance matrix Statistics Algebra Linear algebra
268	Entrenchment Problem, Corporate Governance Mechanisms and Firm Value JUHA-PEKKA KALLUNKI University of Oulu, Department of Accounting and Finance P.O. Box 4600, FINUniversity of Oulu, Finland. Add to Reading List Source URL: cc.oulu.fi Language: English - Date: 2010-12-20 06:09:52 Management Corporate governance Mergers and acquisitions Joint audit Audit Stock Principal–agent problem Management entrenchment Corporation Business Corporations law Auditing
269	Package ‘corrgram’ February 19, 2015 Type Package Title Plot a Correlogram Version 1.7 Date Add to Reading List Source URL: cran.r-project.org Language: English Covariance and correlation Multivariate statistics Statistical dependence Data analysis Principal component analysis Singular value decomposition Matrix Correlation and dependence SAS Statistics Information Science
270	Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystr¨ om Method David G. Anderson Department of Mathematics Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2015-03-26 11:57:44 Numerical linear algebra Singular value decomposition Matrices Matrix theory Matrix Orthogonal matrix Rank Principal component analysis QR decomposition Algebra Linear algebra Mathematics

UPDATE