# Principal component analysis

@article{Abdi2010PrincipalCA, title={Principal component analysis}, author={Herv{\'e} Abdi and Lynne J. Williams}, journal={Wiley Interdisciplinary Reviews: Computational Statistics}, year={2010}, volume={2} }

Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross…

## 4,095 Citations

Introduction to principal components analysis.

- Computer SciencePM & R : the journal of injury, function, and rehabilitation
- 2014

Principal Component Analysis

- MathematicsInformation Fusion and Data Science
- 2020

This chapter shows how PCA arises naturally as the maximum likelihood solution to a particular form of a linear-Gaussian latent variable model, which is called probabilistic principal component analysis.

On the dependency between principal components: Application to determine the rank of a matrix in an evolutionary process

- Computer ScienceJournal of Chemometrics
- 2018

A new method for exploring the dependency between principal components of an evolutionary process is proposed and it showed that MIC could provide accurate estimation of chemical rank in the reasonable timescale rather than DC and also the published rank estimation methods, in the most situations.

Application of Multi-Dimensional Principal Component Analysis to Medical Data

- Computer Science
- 2012

The multi-dimensional PCA is applied to theMulti-dimensional medical data including the functional independence measure (FIM) score, and the results of experimental analysis are described.

Principal Component Analysis and Quasar Identication Techniques

- Computer Science
- 2016

This paper will focus on the PCA of a correlation matrix in order to extract emission line ratios most relevant to the classication.

For Peer Review Partial least squares regression

- Computer Science
- 2009

Partial least squares (PLS) regression is a recent technique that combines features from and generalizes principal component analysis (PCA) and multiple linear regression and extracts from the predictors a set of orthogonal factors called latent variables which have the best predictive power.

A Study of Effectiveness of Principal Component Analysis on Different Data Sets

- Computer Science
- 2017

This paper has taken 24 benchmark data sets from the University of California, Irvine (UCI) Machine Learning Repository and KEEL data set repository and shown how much information is retained by individual PC to show the effectiveness of PCA.

A Principal Component Analysis Algorithm Based on Dimension Reduction Window

- Computer ScienceIEEE Access
- 2018

A novel algorithm named DRWPCA is developed, inspired by the content of the correlation coefficient part of the digital feature of a random variable, and the sliding window model for traffic control in network engineering, that provides promising accuracy, higher ability to reduce dimension and preserves the original information of the data.

MFAg: a R package for carrying out the multiple factor analysis

- Mathematics
- 2017

In considering the study between groups of variables using a multivariate approach, the usual techniques are either limited or unviable to describe how distinct these groups are. The multiple factor…

Principals about principal components in statistical genetics

- BiologyBriefings Bioinform.
- 2019

The possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection are focused on.

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