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Principal value
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291	THE HUB NEWS 2015 We value Relationships and Friendship, Responsibility and Respect Julie Gallaher, Principal TERM 1 - WEEK 10 Sarah Magnusson, Deputy Principal Add to Reading List Source URL: www.ahs.sa.edu.au Language: English - Date: 2015-04-02 00:37:47 Aberfoyle Park High School South Australia States and territories of Australia Aberfoyle Park South Australia NAPLAN Out of School Care and Recreation
292	Principal Research Results Lifetime Evaluation for Distribution Transformers with Over Loaded Condition Background Due to a recent cost reduction request and equipment investment suppression in the electric utility, mor Add to Reading List Source URL: criepi.denken.or.jp Language: English - Date: 2008-09-29 03:24:03 Transformers Transformer Load profile Hysteresis Polymer Distribution transformer Insulator R-value Electromagnetism Physics Electrical engineering
293	Command Line Interface, Stochastic SVD∗ Contents 1 Background information 1.1 Add to Reading List Source URL: mahout.apache.org Language: English - Date: 2014-03-08 01:22:19 Singular value decomposition Multivariate statistics Numerical linear algebra Matrix theory Principal component analysis Rank Latent semantic analysis Matrix QR decomposition Algebra Linear algebra Mathematics
294	2.13 Rotated principal component analysis [Book, Sect[removed]Fig.: PCA applied to a dataset composed of (a) 1 cluster, (b) 2 clusters, (c) and (d) 4 clusters. In (c), an orthonormal rotation ˜j , (j =1, 2). and (d) an ob Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-09-03 18:40:48 Multivariate statistics Singular value decomposition Abstract algebra Matrix theory Principal component analysis Eigenvalues and eigenvectors Factor analysis Rotation matrix Orthonormality Algebra Mathematics Linear algebra
295	694 Journal of the American Statistical Association, June 2009 Discussion Boaz NADLER Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2009-06-16 05:02:26 Data analysis Singular value decomposition Covariance and correlation Principal component analysis Linear algebra Eigenvalues and eigenvectors Covariance matrix Matrix Variance Statistics Algebra Multivariate statistics
296	C:/Papers/SAM_MDL/MDL.dvi Add to Reading List Source URL: www.research.ibm.com Language: English - Date: 2012-08-13 11:38:52 Linear algebra Multivariate statistics Statistical classification Singular value decomposition Abstract algebra Principal component analysis Tikhonov regularization Linear discriminant analysis Large margin nearest neighbor Algebra Statistics Mathematics
297	Ch.2 Principal component analysis (PCA) Books on PCA by Jolliffe (2002), Preisendorfer[removed]PCA also called empirical orthogonal function (EOF) analysis. 2.1 Geometric approach to PCA [Book, Sect[removed]Dataset with Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-08-25 01:58:52 Data analysis Singular value decomposition Covariance and correlation Multivariate statistics Linear algebra Principal component analysis Covariance Eigenvalues and eigenvectors Variance Statistics Algebra Mathematics
298	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
299	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
300	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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