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A robust principal component analysis
Journal of Multivariate Analysis, 1981 L'auteur propose une analyse en composantes principales robuste. La méthode, fondée sur des estimateurs de dispersion robustes, est intéressante; des propriétés asymptotiques sont étudiées sous certaines conditions. Malheureusement, il ne semble pas très aisé, a priori, d'étendre ce travail à d'autres distributions que les bivariées.
F.H. Ruymgaart
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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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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
John Wright +3 more
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Retraction: Craniofacial similarity analysis through sparse principal component analysis [PDF]
PLoS ONEPLOS One Editors
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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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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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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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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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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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