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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
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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.
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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
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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
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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
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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
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Principal components for allometric analysis
American Journal of Physical Anthropology, 1983AbstractLogarithmic 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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Introduction to Principal Components Analysis
PM&R, 2014Principal components analysis (PCA) is a powerful statistical tool that can help researchers analyze datasets with many highly related predictors. PCA is a data reduction technique— that is, it reduces a larger set of predictor variables to a smaller set with minimal loss of information.
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On coMADs and Principal Component Analysis
2019Principal Component Analysis (PCA) is a popular method for linear dimensionality reduction. It is often used to discover hidden correlations or to facilitate the interpretation and visualization of data. However, it is liable to suffer from outliers. Strong outliers can skew the principal components and as a consequence lead to a higher reconstruction ...
Daniyal Kazempour +2 more
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