Results 11 to 20 of about 644,521 (264)
Stock price prediction using principal components.
PLoS ONE, 2020 The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set.
Mahsa Ghorbani, Edwin K P Chong
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Principal components analysis for mixtures with varying concentrations
Modern Stochastics: Theory and Applications, 2021 Principal Component Analysis (PCA) is a classical technique of dimension reduction for multivariate data. When the data are a mixture of subjects from different subpopulations one can be interested in PCA of some (or each) subpopulation separately.
Olena Sugakova, Rostyslav Maiboroda
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Power swing detecting method using principal components analysis
Energy Reports, 2021 During power swing, the distance protection is easily affected by the oscillations of voltage and current which may lead to the mal-operation of the protection. Therefore, a power swing detecting unit is needed to cooperate with distance protection.
Hao Wang +7 more
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Modal Principal Component Analysis [PDF]
Neural Computation, 2020 Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Keishi Sando, Hideitsu Hino
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Principal Component Projection Without Principal Component Analysis
CoRR, 2016 We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression.
Roy Frostig +3 more
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Principal Components of the Meaning
CoRR, 2020 In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space. This space is constructed using principal component analysis (singular decomposition) on the matrix of word category relative information gains, where the categories are those used by the Web of Science, and the words are taken from a reduced ...
Neslihan Suzen +3 more
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The classification of concentration of mixture of analytes
Lietuvos Matematikos Rinkinys, 2004 This paper presents a system which is used for the classification of biosensor signals. The proposed system is applied to the the synthesized and experimental data. The developed system showed good prediction perfomance.
Romas Baronas +3 more
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Characteristics of Principal Components in Stock Price Correlation
Frontiers in Physics, 2021 The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix of assets returns.
Wataru Souma
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Principal Component Regression by Principal Component Selection
Communications for Statistical Applications and Methods, 2015 Abstract We propose a selection procedure of principal components in principal component regression. Our methodselects principal components using variable selection procedures instead of a small subset of major principalcomponents in principal component regression. Our procedure consists of two steps to improve estimation andprediction.
Hosung Lee, Yun Mi Park, Seokho Lee
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Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
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