Results 11 to 20 of about 569,117 (264)
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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A Generalization of Principal Component Analysis [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem.
Samuele Battaglino, Erdem Koyuncu
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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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Euler Principal Component Analysis [PDF]
International Journal of Computer Vision, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stephan Liwicki +3 more
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Population structure identification of Turkmen and Darehshori horses using PCA, DAPC, and SPC methods [PDF]
مجله بیوتکنولوژی کشاورزی, 2022 ObjectiveConservation of the genetic diversity of indigenous animals is very important. For the sustainable use of genetic resources, it is necessary to first study the genetic structure of populations.
Ghazaleh Javanmard +5 more
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Extended Principal Component Analysis
CoRR, 2021 Principal Component Analysis (PCA) is a transform for finding the principal components (PCs) that represent features of random data. PCA also provides a reconstruction of the PCs to the original data. We consider an extension of PCA which allows us to improve the associated accuracy and diminish the numerical load, in comparison with known techniques ...
Pablo Soto-Quiros, Anatoli Torokhti
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Investigation of Morphological Diversity and Evaluation of Tomato Lines Yield Using Multivariate Statistical Analysis [PDF]
Majallah-i ̒Ulum-i Bāghbānī, 2022 Introduction Tomato is a product with a wide range of genotypes with different yields and selection based on this trait and its components can accelerate the breeding programs of this plant.
S. Golcheshmeh +3 more
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FACIAL EXPRESSION RECOGNITION BASED ON PRINCIPAL COMPONENTS ANALYSIS [PDF]
International Journal of Intelligent Computing and Information Sciences, 2016 Recognizing facial expression is one of the most effective applications of image processing and has obtained great attention in latest years. A recognition system for facial expression is a computer based application which detects an individual facial ...
A. Hewa, O. Nomir, A. Saleh
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Identification of dietary patterns by principal component analysis in schoolchildren in the South of Brazil and associated factors [PDF]
Revista Brasileira de Saúde Materno Infantil, 2020 Objectives: to identify dietary patterns (DP) and associated factors in first grade school-children in elementary schools in the South of Brazil. Methods: school-based cross-sectional study, with a non-probabilistic sample of 782 schoolchildren aged 6 ...
Gabriela Rodrigues Bratkowski +3 more
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ANOVA bootstrapped principal components analysis for logistic regression
Croatian Review of Economic, Business and Social Statistics, 2022 Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal components is selected to be used in a classification or a regression model to boost accuracy.
Toleva Borislava
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