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Robust Principal Component Analysis? [PDF]

open access: yesJACM, 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
core   +3 more sources

Optimization of on-line principal component analysis [PDF]

open access: green, 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
openalex   +4 more sources

Principal Component Analysis versus Factor Analysis [PDF]

open access: yesZeszyty 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
doaj   +1 more source

Online Tensor Robust Principal Component Analysis

open access: yesIEEE 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
doaj   +1 more source

Robust Bilinear Probabilistic Principal Component Analysis

open access: yesAlgorithms, 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
doaj   +1 more source

Principal Component Analysis [PDF]

open access: yes, 2021
This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis.

core   +4 more sources

JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit

open access: yesBMC 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
doaj   +1 more source

Structured illumination microscopy based on principal component analysis

open access: yeseLight, 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]

open access: yesEngineering 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
doaj   +1 more source

A principal component analysis in concrete design

open access: yesBudownictwo 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
doaj   +1 more source

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