aim and background: the aim of the present study was to develop a short form of the persian version of the cognitive emotion regulation questionnaire (cerq-p-short) and to examine its reliability and validity. methods and materials: the cerq-p was administrated to 420 (220 male and 200 female) iranian university students in 2009-2010 academic year. following stepwise omission of the items with ...

Principal component analysis (PCA) has been applied to analyze random fields in various scientific disciplines. However, the explainability of PCA remains elusive unless strong domain-specific knowledge is available. This paper provides a theoretical framework that builds duality between eigenmodes field and eigenstates Schr\"odinger equation. Based on we propose algorithm replace expensive sol...

in the present study, quantitative relationships between molecular structure and anti-tubercular activity of some 5-methyl/trifluoromethoxy-1 h -indole-2,3-dione-3-thiosemicarbazone derivatives were discovered. the detailed application of an efficient linear method and principal component regression (pcr) for the evaluation of quantitative structure activity relationships of the studied compound...

the relationship between live body weight, body length, girth circumference, animal hight, upper, middle as well as lower width of fat-tail, fat-tail length, fat-tail gap length, fat-tail depth and fat-tail circumference along with fat-tail weight were determined using records of 731 loribakhtiari sheep. principal component and least square analyses were applied to solve the collinearity instab...

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Mixtures of Principal Component Analyzers can be used to model high dimensional data that lie on or near a low dimensional manifold. By linearly mapping the PCA subspaces to one global low dimensional space, we obtain a ‘global’ low dimensional coordinate system for the data. As shown by Roweis et al., ensuring consistent global low-dimensional coordinates for the data can be expressed as a pen...

A. Two quite different forms of nonlinear principal component analysis have been proposed in the literature. The first one is associated with the names of Guttman, Burt, Hayashi, Benzécri, McDonald, De Leeuw, Hill, Nishisato. We call itmultiple correspondence analysis. The second form has been discussed by Kruskal, Shepard, Roskam, Takane, Young, De Leeuw, Winsberg, Ramsay. We call it no...

Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best reconstruction for an original data in the mean squared error sense. In this paper, the progressive PCA (PrPCA) is proposed, which could progressively extract features from a set of given data with large dimensionality and the e...

Principal component analysis (PCA) is a dimensionality reduction modeling technique that transforms a set of process variables by rotating their axes of representation. Maximum Likelihood PCA (MLPCA) is an extension that accounts for different noise contributions in each variable. Neither PCA nor its extensions utilize external information about the model or data such as the range or distributi...

In many experiments, the data points collected live in high-dimensional observation spaces, yet can be assigned a set of labels or parameters. In electrophysiological recordings, for instance, the responses of populations of neurons generally depend on mixtures of experimentally controlled parameters. The heterogeneity and diversity of these parameter dependencies can make visualization and int...