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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1960 1
1963 1
1964 1
1991 1
1994 1
1999 2
2000 2
2001 41
2002 143
2003 291
2004 499
2005 723
2006 850
2007 1089
2008 1345
2009 1625
2010 1989
2011 2290
2012 2621
2013 2700
2014 2608
2015 2527
2016 2327
2017 2266
2018 2309
2019 1226
2020 43
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25,150 results
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Page 1
Principal component analysis: a method for determining the essential dynamics of proteins.
David CC and Jacobs DJ. Methods Mol Biol 2014. PMID 24061923 Free PMC article.
It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. ...
It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is mor …
Interpretable functional principal component analysis.
Lin Z, et al. Biometrics 2016. PMID 26683051
Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. ...
Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curv …
Parametric functional principal component analysis.
Sang P, et al. Biometrics 2017. PMID 28295173
Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). Most existing FPCA approaches use a set of flexible basis functions such as B-spline basis to represent the FPCs, and control the smoothness of the FPCs by adding roughness penalties. ...
Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variatio …
Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability.
Jao PK, et al. Conf Proc IEEE Eng Med Biol Soc 2018. PMID 30440781
We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variability. Assuming that subjects undergo the same cognitive process or perform the same task in a short period (e.g., a few seconds), as a result, the signal of interest should be represented by only a few components. ...
We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variability. Assuming that subj …
Principal component analysis for designed experiments.
Konishi T. BMC Bioinformatics 2015. PMID 26678818 Free PMC article.
BACKGROUND: Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. ...The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes. ...
BACKGROUND: Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and …
Principal Component Analysis based on Nuclear norm Minimization.
Mi JX, et al. Neural Netw 2019. PMID 31228720
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. ...Third, if some elements of a sample are disturbed, to extract principal components (PCs) by directly projecting data with transformation matrix causes incorrect mapping of sample to its genuine location in low-dimensional feature subspace. ...
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer
Principal component analysis-based features generation combined with ellipse models-based classification criterion for a ventricular septal defect diagnosis system.
Sun S and Wang H. Australas Phys Eng Sci Med 2018. PMID 30238221
In this study, a simple and efficient diagnostic system, which adopts a novel methodology consisting of principal component analysis (PCA)-based feature generation and ellipse models-based classification criterion, is proposed for the diagnosis of a ventricular septal defect (VSD). ...
In this study, a simple and efficient diagnostic system, which adopts a novel methodology consisting of principal component analysis …
Adaptive robust principal component analysis.
Liu Y, et al. Neural Netw 2019. PMID 31401529
Robust Principal Component Analysis (RPCA) is a powerful tool in machine learning and data mining problems. However, in many real-world applications, RPCA is unable to well encode the intrinsic geometric structure of data, thereby failing to obtain the lowest rank representation from the corrupted data. ...
Robust Principal Component Analysis (RPCA) is a powerful tool in machine learning and data mining problems. However, in many real-wor …
Principal polynomial analysis.
Laparra V, et al. Int J Neural Syst 2014. PMID 25164247
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of maximal variance by means of curves, instead of straight lines. ...
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonl …
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