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Principal component analysis
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#	Item
881	Automatic Group Sparse Coding Add to Reading List Source URL: www.research.ibm.com Language: English - Date: 2012-08-13 11:38:19 Data analysis Multivariate statistics Mathematical optimization Matrix Sparse matrix Principal component analysis Lagrange multiplier Sheaf Machine learning Algebra Mathematics Statistics
882	CONTENTS Page CHAPTER 1: INTRODUCTION. Add to Reading List Source URL: www.cru.uea.ac.uk Language: English - Date: 2009-12-22 04:44:02 Multivariate statistics Covariance and correlation Paleoclimatology Data analysis Principal component analysis Singular value decomposition Climatology El Niño-Southern Oscillation Correlation and dependence Atmospheric sciences Statistics Meteorology
883	CLASSIFICATION OF MOVEMENT EEG WITH LOCAL DISCRIMINANT BASES Nuri Firat Ince, Ahmed Tewfik, Sami Arica Department of Electrical and Electronics Engineering, University of Cukurova Department of Electrical Engineering, U Add to Reading List Source URL: www.dtc.umn.edu Language: English* - Date: 2012-08-16 12:29:03 Brain–computer interface Linear discriminant analysis Principal component analysis Feature extraction Segmentation Dimension reduction Statistics Multivariate statistics Electroencephalography
884	Feature List (Updated for version 8.6 on May 18, [removed]Bit Support Overview ▫ Native 64-bit and 32-bit applications Add to Reading List Source URL: www.silverdalescientific.co.uk Language: English - Date: 2012-06-07 06:03:22 Earth sciences graphics software Origin Microsoft Excel Principal component analysis NetCDF Mathematica Plot Contour line Spreadsheet Software Statistics Mathematical software
885	Principal component analysis (PCA) 2.6 Scaling the PCs and eigenvectors [Book, Sect[removed]Various options for scaling the PCs {aj (t)} and the eigenvectors {ej }. One can introduce an arbitrary scale factor α, aj0 = Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-08-20 18:42:54 Matrix theory Singular value decomposition Abstract algebra Data analysis Eigenvalues and eigenvectors Principal component analysis Matrix Covariance matrix Vector space Algebra Linear algebra Mathematics
886	JOURNAL OF NETWORKS, VOL. 9, NO. 12, DECEMBER[removed]A Hybrid Classiﬁer Using Reduced Signatures for Automated Soft-Failure Diagnosis in Add to Reading List Source URL: titania.ctie.monash.edu.au Language: English - Date: 2014-12-30 00:33:18 Principal component analysis Feature extraction Dimension reduction Linear discriminant analysis Eigenvalues and eigenvectors Scatter matrix Mahalanobis distance Statistics Multivariate statistics Algebra
887	EOSC 510 Winter session[removed]term 1) 3 credits Course title: Data Analysis in Atmospheric, Earth & Ocean Sciences This is an online graduate course on applying machine learning and statistical methods to environmen Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2014-02-04 15:18:21 Pattern recognition Kernel methods Data mining Principal component analysis Neural network Statistical classification Decision tree learning Regularization Regression analysis Statistics Machine learning Science
888	Michael C. Neale MX: Statistical Modeling by Add to Reading List Source URL: www.vipbg.vcu.edu Language: English - Date: 2004-04-15 19:14:24 Statistical methods Data analysis Regression analysis Structural equation modeling Matrix Principal component analysis Twin study Covariance matrix Linear algebra Statistics Multivariate statistics Econometrics
889	Using a Single Usability Metric (SUM) to Compare the Usability of Competing Products Jeff Sauro Erika Kindlund Add to Reading List Source URL: www.measuringu.com Language: English - Date: 2011-08-26 19:26:15 Usability Science Technical communication Technology Human factors Metric Normal distribution Principal component analysis Ergonomics Statistics Human–computer interaction Systems psychology
890	Chapter 2 lecture questions Q1: “Prove that C is a real, symmetric, positive semi-definite matrix” requires us to prove that for any vector v 6= 0, it follows that vT Cv ≥ 0. Proof: vT Cv = vT E[(y − y)(y − y)T Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-10-04 14:07:27 Singular value decomposition Matrices Mathematics Trigonometry Rotation matrix Linear algebra Data analysis Principal component analysis

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