Results 11 to 20 of about 2,126,918 (327)

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
AbstractA robust principal component analysis for samples from a bivariate distribution function is described. The method is based on robust estimators for dispersion in the univariate case along with a certain linearization of the bivariate structure.
F.H. Ruymgaart
openalex   +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
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Reionization constraints using Principal Component Analysis [PDF]

, 2005
Using a semi-analytical model developed by Choudhury & Ferrara (2005) we study the observational constraints on reionization via a principal component analysis (PCA).
Mitra, Sourav, Choudhury, T. Roy, FERRARA, A.   +3 more
core   +7 more sources

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

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

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, Jiazhen Li, Weiqi Liu, Jinye Peng, Z. Jane Wang   +4 more
doaj   +1 more source

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, Hind Restom Mohammed, Hayder Abood kouree   +2 more
doaj   +1 more source

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, Navya Nagananda, Andreas Savakis   +2 more
doaj   +1 more source

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