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- ...pca.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/601-pca.pdf Slides in PDF]. * Murphy Chap 12. PCA is not covered in Mitchell.1 KB (188 words) - 15:29, 21 April 2016
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- ...pca.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/601-pca.pdf Slides in PDF]. * Murphy Chap 12. PCA is not covered in Mitchell.1 KB (188 words) - 15:29, 21 April 2016
- * [http://www.cs.cmu.edu/~wcohen/10-601/pca+mf.pdf Slides in PDF]. * Murphy Chap 12. PCA is not covered in Mitchell.1 KB (238 words) - 15:33, 21 April 2016
- * How PCA, SVD, k-means, and other clustering methods relate to matrix factorization.617 bytes (88 words) - 11:45, 31 October 2013
- They also explained the difference between this algorithm and [[UsesMethod::PCA]], where ...ASO]] find principle components in the predictor space while [[UsesMethod::PCA]] seeks in data space.2 KB (357 words) - 05:46, 23 November 2010
- ...tures from each dimension of the network via modularity maximization; then PCA is applied on the concatenated data to select the top eigenvectors. Afterwa3 KB (423 words) - 18:43, 31 March 2011
- ...atistical tools are used: 1) [[UsesMethod::Principal Component Analysis]] (PCA) for grouping of variables responsible for the popularity variation, 2) Cor 1. By applying Principal Component Analysis (PCA) on each of the 48 collected clone sets, the two primary components roughly4 KB (542 words) - 21:39, 2 November 2012
- ...d 11/12 ('''Wm''') || Thur 11/13 (Wm) || [[10-601 Matrix Factorization|PCA and Matrix Factorization]]|| ''slides to be updated'' || [http://www.cs.cmu ...and Network Models]] || || HW8: Topic models (worksheet, experiments with PCA code) - due 11/24. Kuo Liu and Yipei Wang.8 KB (1,059 words) - 10:37, 3 December 2014
- ...nt for temporal varying factors that impact popularity, while Paper 2 uses PCA, correlation analysis techniques, and a multi-linear regression model for a2 KB (304 words) - 14:38, 5 November 2012
- | W 4/13 || [[10-601_Matrix_Factorization|PCA and dimension reduction]] || William || HW7: Deep Learning || TBA3 KB (339 words) - 10:21, 12 January 2016
- ...l matrix <math>L_{ij} = l(y_i, y_j)</math>. Perform a kernel [[UsesMethod::PCA]]. The eigenvectors corresponding to the top 'n' eigenvalues constitute the3 KB (431 words) - 00:42, 1 December 2011
- | W 4/13 || [[10-601_PCA|PCA and dimension reduction]] || William || HW7: Deep Learning || Zichao4 KB (549 words) - 21:14, 5 September 2016