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Principal components analysis of population admixture. [PDF]
PLoS ONE, 2012 With the availability of high-density genotype information, principal components analysis (PCA) is now routinely used to detect and quantify the genetic structure of populations in both population genetics and genetic epidemiology.
Jianzhong Ma, Christopher I Amos
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New Interpretation of Principal Components Analysis [PDF]
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki, 2017 A new look on the principal component analysis has been presented. Firstly, a geometric interpretation of determination coefficient was shown.
Zenon Gniazdowski
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Robust principal component analysis? [PDF]
Journal of the ACM, 2011 This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly
Candès, Emmanuel J. +3 more
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A practical introduction to EEG Time-Frequency Principal Components Analysis (TF-PCA) [PDF]
Developmental Cognitive Neuroscience, 2022 This EEG methods tutorial provides both a conceptual and practical introduction to a promising data reduction approach for time-frequency representations of EEG data: Time-Frequency Principal Components Analysis (TF-PCA).
George A. Buzzell +3 more
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mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis [PDF]
Genome Biology, 2022 The analysis of microbiome data has several technical challenges. In particular, count matrices contain a large proportion of zeros, some of which are biological, whereas others are technical.
Yanyan Zeng +4 more
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Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis. [PDF]
Front Cell Neurosci, 2017 It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes.
Fernández-Arjona MDM +4 more
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Introduction to Principal Components Analysis
PM&R, 2014 Principal 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.
Kristin L. Sainani
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Principal components analysis in clinical studies. [PDF]
Ann Transl Med, 2017 Zhang Z, Castelló A.
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A multi-dimensional functional principal components analysis of EEG data. [PDF]
Biometrics, 2017 Hasenstab K +6 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 ...
Sando, Keishi, Hino, Hideitsu
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