<--- Back to Details
First PageDocument Content
Matrix theory / Singular value decomposition / Numerical linear algebra / Multivariate statistics / Matrix / Principal component analysis / Eigenvalues and eigenvectors / Diagonal matrix / Factor analysis / Algebra / Linear algebra / Mathematics
Date: 2012-06-14 10:25:58
Matrix theory
Singular value decomposition
Numerical linear algebra
Multivariate statistics
Matrix
Principal component analysis
Eigenvalues and eigenvectors
Diagonal matrix
Factor analysis
Algebra
Linear algebra
Mathematics

HOW TO USE THE BLACK BOX by Keith T. Poole Graduate School of Industrial Administration

Add to Reading List

Source URL: www.voteview.com

Download Document from Source Website

File Size: 119,84 KB

Share Document on Facebook

Similar Documents

Principal Component Analysis on non-Gaussian Dependent Data  Fang Han Johns Hopkins University, 615 N.Wolfe Street, Baltimore, MDUSA Han Liu Princeton University, 98 Charlton Street, Princeton, NJUSA

Principal Component Analysis on non-Gaussian Dependent Data Fang Han Johns Hopkins University, 615 N.Wolfe Street, Baltimore, MDUSA Han Liu Princeton University, 98 Charlton Street, Princeton, NJUSA

DocID: 1vdC9 - View Document

Generalized Principal Component Analysis (GPCA)∗ Ren´e Vidal† Yi Ma‡ Shankar Sastry† † Department of EECS, University of California, Berkeley, CA 94720

Generalized Principal Component Analysis (GPCA)∗ Ren´e Vidal† Yi Ma‡ Shankar Sastry† † Department of EECS, University of California, Berkeley, CA 94720

DocID: 1uYRE - View Document

A. Krisciukaitis et al.: Efficiency Evaluation of Autonomic Heart Control by Using the Principal Component Analysis of ECG P-Wave, en 9 en9  Efficiency Evaluation of Autonomic Heart Control by Using

A. Krisciukaitis et al.: Efficiency Evaluation of Autonomic Heart Control by Using the Principal Component Analysis of ECG P-Wave, en 9 en9 Efficiency Evaluation of Autonomic Heart Control by Using

DocID: 1tOkO - View Document

Full Regularization Path for Sparse Principal Component Analysis Alexandre d’Aspremont, Francis Bach & Laurent El Ghaoui, Princeton University, INRIA/ENS Ulm & U.C. Berkeley  Support from NSF, DHS and Google.

Full Regularization Path for Sparse Principal Component Analysis Alexandre d’Aspremont, Francis Bach & Laurent El Ghaoui, Princeton University, INRIA/ENS Ulm & U.C. Berkeley Support from NSF, DHS and Google.

DocID: 1tMMK - View Document

Binary Principal Component Analysis in the Netflix Collaborative Filtering Task

Binary Principal Component Analysis in the Netflix Collaborative Filtering Task

DocID: 1tMJC - View Document