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Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis [PDF]
ITM Web of ConferencesNonlinear Principal Component Analysis (PRINCALS) is an extension of Principal Component Analysis (Linear), which can reduce the variables of mixed scale multivariable data (nominal, ordinal, interval, and ratio) simultaneously.
Makkulau +4 more
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A Low-Complexity Quantum Principal Component Analysis Algorithm
IEEE Transactions on Quantum Engineering, 2022 In this article, we propose a low-complexity quantum principal component analysis (qPCA) algorithm. Similar to the state-of-the-art qPCA, it achieves dimension reduction by extracting principal components of the data matrix, rather than all components of
Chen He +4 more
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Anal. Methods, 2014 Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas.
Rasmus Bro, Age K. Smilde, Age K. Smilde
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Tutorial on principal component analysis, with applications in R [PDF]
, 2021 This tutorial reviews the main steps of the principal component analysis of a multivariate data set and its subsequent dimensional reduction on the grounds of identified dominant principal components. The underlying computations are demonstrated and performed by means of a script written in the statistical software package R.
arxiv +1 more source
Nucleic Acids Res., 2015 The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web
Tauno Metsalu, J. Vilo
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Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization [PDF]
Computer Vision and Pattern Recognition, 2016 This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA [4] to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) [14] and its induced tensor tubal rank and tensor ...
Canyi Lu +5 more
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Principal Component Analysis of Infertility Data
Journal of Kufa for Mathematics and Computer, 2013 This paper applied PCA on infertility set of data, that was collected from Al-Nasiriya  province. Infertility of women that have been unable to conceive a child after one year of their marriage without birth control. Since infertility is very common
Nazera Khalil Dakhil +2 more
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GrIP-PCA: Grassmann Iterative P-Norm Principal Component Analysis
IEEE Open Journal of Signal Processing, 2020 Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the L2-norm, which can be highly influenced by outlier data.
Breton Minnehan +2 more
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Quantifying Topographic Ruggedness Using Principal Component Analysis
Advances in Civil Engineering, 2021 The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications.
Maan Habib
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Probabilistic Principal Component Analysis
, 1999 Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of observed data vectors may be determined through maximum ...
Michael E. Tipping, Charles M. Bishop
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