Results 1 to 10 of about 744,201 (267)

A study of principal component analysis applied to spatially distributed wind power

, 2011
Multivariate dimension reduction schemes could be very useful in limiting the number of random statistical variables needed to represent distributed wind power spatial diversity in transmission integration studies.
Burke, Daniel J., Mark J. O'Malley, Daniel J. Burke, O\u27Malley, Mark, O'Malley, Mark   +4 more
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Generalized Principal Component Analysis

CoRR, 2019
Generalized principal component analysis (GLM-PCA) facilitates dimension reduction of non-normally distributed data. We provide a detailed derivation of GLM-PCA with a focus on optimization. We also demonstrate how to incorporate covariates, and suggest post-processing transformations to improve interpretability of latent factors.
openaire   +2 more sources

Robust sparse principal component analysis. [PDF]

A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret.
Croux, Christophe, Fritz, Heinrich, Filzmoser, Peter   +2 more
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Principal-component analysis–coefficients of linear combinations.

, 2019
Principal-component analysis–coefficients of linear combinations.
Tiago Santos Telles (6401954), Carlos Eduardo Caldarelli (7027019), Marcia Regina Gabardo da Camara (7027022), Bruno Volsi (6401945)   +3 more
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A least squares approach to Principal Component Analysis for interval valued data [PDF]

Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components.
D'Urso, Pierpaolo, Giordani, Paolo
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Performance monitoring of MPC based on dynamic principal component analysis

, 2011
A unified framework based on the dynamic principal component analysis (PCA) is proposed for performance monitoring of constrained multi-variable model predictive control (MPC) systems.
Gong Quan Chen, Sheng Chen, Chen, Sheng, Tian, Xuemin, Cao, Yuping, Yu Ping Cao, Xue Min Tian, Chen, Gongquan   +7 more
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A generalization of the principal component analysis

Journal of the Japan Statistical Society, Japanese Issue, 1988
A nonlinear generalization of the principal component analysis (PCA) is made under normality. It is shown that this generalized PCA problem leads to an eigenvalue problem for the Hadamard products of the correlation matrix. In the framework of the generalized PCA, the result is applied to the problem of finding square-integrable continuous ...
Kariya Takeaki, Otsu Tastuo, TAKAYUKI SAITO   +2 more
openaire   +2 more sources

Generalized power method for sparse principal component analysis [PDF]

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of
Journée, Michel, Nesterov, Yurii, Richtarik, Peter, Sepulchre, Rodolphe   +3 more
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Investigation of factor rotation routines in principal component analysis of stock returns

, 2014
Includes bibliographical references.This paper investigates rotation routines that will produce uncorrelated rotated principal components for a dataset of stock returns, in an attempt to identify the macroeconomic factors that best explain the ...
Weimar, Nicole
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The importance of selecting the optimal number of principal components for fault detection using principal component analysis

, 2012
Includes summary.Includes bibliographical references.Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization.
Khwambala, Patricia Helen
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