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 - Orthonormality - Document - PDFSEARCH.IO

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	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 Document Date: 2013-09-03 18:40:48 Open Document File Size: 633,89 KB Share Result on Facebook Position Prime Minister / RT / / SocialTag Multivariate statistics Singular value decomposition Abstract algebra Matrix theory Principal component analysis Eigenvalues and eigenvectors Factor analysis Rotation matrix Orthonormality Algebra

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 obAdd to Reading List