What is Principal Component Analysis

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Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance. Learn more in: Multitarget Classifiers for Mining in Bioinformatics

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Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance. Learn more in: System Theory: From Classical State Space to Variable Selection and Model Identification

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It is unsupervised dimension reduction approach for large database size. It is used to find the Eigen values for face recognition. Learn more in: Cloud Security Using Ear Biometrics

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Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance. Learn more in: Machine Learning Through Data Mining

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A statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components. Learn more in: A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages

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It is unsupervised dimension reduction approach for large database size. It is used to find the eigen values for face recognition. Learn more in: Swarm Intelligence for Biometric Feature Optimization

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It’s a method of analysis which involves finding the linear combination of a set of variables that has maximum variance and removing its effect and repeating this successively. Learn more in: Hybrid Ensemble Learning Methods for Classification of Microarray Data: RotBagg Ensemble Based Classification

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A technique used to reduce multidimensional data sets to lower dimensions for analysis. PCA involves the computation of the eigenvalue decomposition of a data set, usually after mean centering the data for each attribute. Learn more in: Neural Networks on Handwritten Signature Verification

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( PCA): Used to find global feature from image /frame. Learn more in: Digital Video Tampering Detection Techniques

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Principal component analysis is a dimensionality reduction technique that reduces multiple (correlated) random variables into smaller number of (uncorrelated and orthogonal) random variables such the explained variance is maximized. Learn more in: Reevaluating Factor Models: Feature Extraction of the Factor Zoo

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Principal component analysis is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Learn more in: Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

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A procedure that uses an orthogonal transform to convert a set of observations of correlated variables into a set of values of linearly uncorrelated variables called principal components. Learn more in: Measuring the Effects of Data Mining on Inference

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Principal component analysis is a statistical approach that transforms a set of observations of possibly correlated variables into a set of linearly uncorrelated principal components using orthogonal transformation. Learn more in: Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

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Is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Learn more in: Visualization Tools for Big Data Analytics in Quantitative Chemical Analysis: A Tutorial in Chemometrics

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Feature extraction technique in which the variance of the data is maximized. It provides a new feature space in which the dimensions are ordered by sample correlation. Thus, a subset of these dimensions can be chosen in which samples are minimally correlated. Learn more in: Component Analysis in Artificial Vision

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Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance. Learn more in: Design and Managing of Distributed Virtual Organizations

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A multivariate projection technique within unsupervised machine learning that can investigate relationships among multiple variables and explain the causes of variance in a data set. This technique linearly transforms an original set of variables into a substantially smaller set of uncorrelated variables called principal components, which represent most of the information in the original data set. Learn more in: A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning

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Multivariate data analysis technique that allows to reduce the dimensionality of a high set of interrelated variables through linear combinations ordered from greater to lesser explanatory capacity of the total variance of the set, and independent of each other. Learn more in: Statistical Techniques for Research