Results 231 to 240 of about 744,201 (267)
Some of the next articles are maybe not open access.
Coupled Principal Component Analysis
IEEE Transactions on Neural Networks, 2004A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire +5 more sources
Directed Principal Component Analysis
Operations Research, 2014We consider a problem involving estimation of a high-dimensional covariance matrix that is the sum of a diagonal matrix and a low-rank matrix, and making a decision based on the resulting estimate. Such problems arise, for example, in portfolio management, where a common approach employs principal component analysis (PCA) to estimate factors used in ...
Yi-Hao Kao, Benjamin Van Roy
openaire +2 more sources
2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D. +3 more
openaire +3 more sources
Kernel Principal Component Analysis
1997A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images.
Bernhard Schölkopf +2 more
openaire +3 more sources
HITS is Principal Components Analysis
The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005In this work, we show that Kleinberg's hubs and authorities model (HITS) is simply principal components analysis (PCA; maybe the most widely used multivariate statistical analysis method), albeit without centering, applied to the adjacency matrix of the graph of Web pages.
Marco Saerens, François Fouss
openaire +1 more source
Principal Component Discriminant Analysis
Statistical Applications in Genetics and Molecular Biology, 2008The approach adopted involved two-stages. First the 11205 measurements in the mass spectrometry data were reduced to 14 scores by a principal component analysis of the centered but otherwise untreated and unscaled data matrix. Then a linear classifier was derived by linear discriminant analysis using these 14 scores as inputs.
openaire +2 more sources
Hausman Principal Component Analysis
2006The aim of this paper is to obtain discrete-valued weights of the variables by constraining them to Hausman weights (−1, 0, 1) in principal component analysis. And this is done in two steps: First, we start with the centroid method, which produces the most restricted optimal weights −1 and 1; then extend the weights to −1,0 or 1.
CHOULAKIAN V. +2 more
openaire +2 more sources
Weighted Principal Component Analysis
2011In this paper, we proposed a weighted PCA (WPCA) method. This method first uses the distances between the test sample and each training sample to calculate the 'weighted' covariance matrix. It then exploits the obtained covariance matrix to perform feature extraction.
Zizhu Fan, Ergen Liu, Baogen Xu
openaire +1 more source
Principal component analysis of dipeptides
Journal of Computational Chemistry, 1994AbstractPrincipal component analysis applied to a set of dipeptides illustrates how changes in families of parameters act in concert to produce overall molecular structural changes. Principal component analysis is an eigenvalue–eigenvector analysis whereby the parametric sensitivity coefficient matrix is manipulated to produce weighted principal ...
Roberta Susnow +2 more
openaire +1 more source
Variants of Principal Components Analysis
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007Principal components analysis (PCA) is probably the most commonly used transform to perform various tasks in many applications. It produces a set of uncorrelated components according to decreasing magnitude of eigenvalues of a second order-statistics covariance matrix.
Weimin Liu, Chein-I Chang
openaire +1 more source