Results 11 to 20 of about 2,231,262 (333)

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., Li, Xiaodong, Ma, Yi, Wright, John   +3 more
core   +3 more sources

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
openalex   +4 more sources

Variance The Estimation Eigen Value of Principal Component Analysis and Nonlinear Principal Component Analysis [PDF]

ITM Web of Conferences
Nonlinear 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, Tenri Ampa Andi, Yahya Irma, La Ome Lilis, Saidi La Ode   +4 more
doaj   +1 more source

Principal component analysis in pig breeds identification

Indian Journal of Animal Sciences, 2023
Maintaining the purity of pig breeds is an essential task for their economic value. The traditional breed identification methods through coat colour are prone to error due to huge intra-breed variation. This paper uses principal component Analysis (PCA)
SANKET DAN, SATYENDRA NATH MANDAL, PRITAM GHOSH, SUBHRANIL MUSTAFI, SANTANU BANIK   +4 more
doaj   +1 more source

Modal Principal Component Analysis [PDF]

Neural Computation, 2020
Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean ...
Hideitsu Hino, Keishi Sando
openaire   +3 more sources

Principal Component Analysis

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
openaire   +5 more sources

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
doaj   +1 more source

Robust Orthogonal Complement Principal Component Analysis [PDF]

, 2016
Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation ...
Li, Shijie, She, Yiyuan, Wu, Dapeng
core   +1 more source

Improved Two-Dimensional Quaternion Principal Component Analysis

IEEE Access, 2019
The two-dimensional quaternion principal component analysis (2D-QPCA) is first improved into abstracting the features of quaternion matrix samples in both row and column directions, being the generalization ability, and with the components weighted by ...
Meixiang Zhao, Zhigang Jia, Dunwei Gong
doaj   +1 more source

Reionization constraints using Principal Component Analysis [PDF]

, 2010
Using a semi-analytical model developed by Choudhury & Ferrara (2005) we study the observational constraints on reionization via a principal component analysis (PCA).
Andrea Ferrara, Barkana, Bolton, Burigana, Chiu, Choudhury, Choudhury, Choudhury, Choudhury, Choudhury, Clarkson, Crittenden, Efstathiou, Efstathiou, Fan, Fan, Furlanetto, Gallerani, Gallerani, Gallerani, Hopkins, Hu, Huterer, Huterer, Kadota, Kashikawa, Lacey, Larson, Leach, Lewis, Loeb, Miralda-Escudé, Mortonson, Munshi, Press, Press, Pritchard, Prochaska, Schneider, Songaila, Sourav Mitra, T. Roy Choudhury, Totani, Wyithe   +43 more
core   +3 more sources