Results 231 to 240 of about 569,117 (264)

Principal Component Discriminant Analysis

Statistical Applications in Genetics and Molecular Biology, 2008
The 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.
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HITS is Principal Components Analysis

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005
In 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
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Hausman Principal Component Analysis

2006
The 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., D'AMBRA, LUIGI, SIMONETTI B.   +2 more
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Weighted Principal Component Analysis

2011
In 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
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Principal components for allometric analysis

American Journal of Physical Anthropology, 1983
AbstractLogarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables.
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Principal component analysis of dipeptides

Journal of Computational Chemistry, 1994
AbstractPrincipal 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, Clarence Schutt, Herschel Rabitz   +2 more
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Variants of Principal Components Analysis

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
Principal 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
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Principal component analysis

Transfusion, 2018
Aaron S. Hess, John R. Hess
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Principal component analysis

2016
Elaine Cristina Borges Scalabrini, Paula Cristina Remoaldo   +1 more
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Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models

Technometrics, 1978
Svante Wold
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