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Tutorial on PCA and approximate PCA and approximate kernel PCA
Artificial Intelligence Review, 2022 AbstractPrincipal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario.
S. Marukatat
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Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition
Frontiers in Computer Science, 2021 Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability.
Weili Guo +6 more
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Canadian Journal of Statistics, 2023 AbstractMahalanobis distance of covariate means between treatment and control groups is often adopted as a balance criterion when implementing a rerandomization strategy. However, this criterion may not work well for high‐dimensional cases because it balances all orthogonalized covariates equally.
Hengtao Zhang +2 more
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Scientific Reports, 2022 Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information.
E. Elhaik
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DWT-PCA based Video Watermarking
Journal of Electronics,Computer Networking and Applied Mathematics, 2022 Progressed watermarking video may be a methodology for embedding additional data another to video salute. Embedded data is utilized for proprietor copyright or recognizable affirmation. It added up to approach for watermarking is shown in this System, by
Swapnil Takale, Dr. Altaaf Mulani
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Arab Journal of Forensic Sciences & Forensic Medicine, 2023 In the ambit of Forensic examination of the questioned documents, writing instruments often serve as an essential tool in disclosing the legitimacy of a document.
Pawan Gupta +3 more
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IEEE Transactions on Geoscience and Remote Sensing, 2022 As hyperspectral imagery (HSI) contains rich spectral and spatial information, a novel principal component analysis (PCA) and segmented-PCA (SPCA)-based multiscale 2-D-singular spectrum analysis (2-D-SSA) fusion method is proposed for joint spectral ...
Hang Fu +4 more
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Journal of Statistical Physics, 2017 A system with many degrees of freedom can be characterized by a covariance matrix; principal components analysis (PCA) focuses on the eigenvalues of this matrix, hoping to find a lower dimensional description. But when the spectrum is nearly continuous, any distinction between components that we keep and those that we ignore becomes arbitrary; it then ...
Bradde, Serena, Bialek, William
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Hybrid modeling and prediction of oyster norovirus outbreaks
Journal of Water and Health, 2021 This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite ...
Shima Shamkhali Chenar, Zhiqiang Deng
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Principal component analysis of a canning determinate tomato collection in the IPGR, Sadovo - Bulgaria [PDF]
Agroznanje, 2012 The success of a tomato breeding programme largely depends on the study of initial material and symptoms studied as well as manifestations of dependence between them. The study was conducted during the period 2008-2011 in the IPGR, Bulgaria.
Krasteva Liliya +2 more
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