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Robust Principal Component Analysis? [PDF]
Journal of the ACM, 2009 This paper 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?
Candes, Emmanuel J. +3 more
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Optimization of on-line principal component analysis [PDF]
, 1999 Different techniques, used to optimise on-line principal component analysis, are investigated by methods of statistical mechanics. These include local and global optimisation of node-dependent learning-rates which are shown to be very efficient in ...
Enno Schlösser +2 more
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Principal Component Analysis versus Factor Analysis [PDF]
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki, 2021 The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed.
Zenon Gniazdowski
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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Robust Bilinear Probabilistic Principal Component Analysis
Algorithms, 2021 Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on the maximum likelihood procedure provides a probabilistic manner to implement dimension reduction ...
Yaohang Lu, Zhongming Teng
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Principal Component Analysis [PDF]
, 2021 This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis.
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JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit
BMC Bioinformatics, 2021 Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to
Charles C. David +2 more
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Structured illumination microscopy based on principal component analysis
eLight, 2023 Structured illumination microscopy (SIM) is one of the powerful super-resolution modalities in bioscience with the advantages of full-field imaging and high photon efficiency.
Jiaming Qian +6 more
semanticscholar +1 more source
Principal Component Analysis Based Wavelet Transform [PDF]
Engineering and Technology Journal, 2012 The principal component analysis (PCA) is a valuable statistical means, implemented in time domain that has found application in many fields such as face recognition and image compression, and is a common technique for finding patterns in data of high ...
Hana M. Salman
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A principal component analysis in concrete design
Budownictwo o Zoptymalizowanym Potencjale Energetycznym, 2022 Over the last 200 years, ordinary concrete has evolved from four basic ingredient materials (gravel, sand, cement, and water) to multicomponent complex composites. The number and variety of the additives, admixtures, non-conventional aggregates, fillers,
Janusz Kobaka, Jacek Katzer
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