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What is Principal Component Analysis
1.
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
2.
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
3.
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
4.
Rearrangement of the data matrix in new orthogonal transformed variables ordered in decreasing order of variance.
Learn more in: Machine Learning Through Data Mining
5.
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 component
s. The number of
principal component
s 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
component
s.
Learn more in: A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages
6.
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
7.
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
8.
A statistical procedure that is used to identify which items in a questionnaire are highly correlated with each other. Clusters of correlated items indicate the presence of an underlying, independent construct or ‘factor’. This procedure can be used to verify expected constructs in a questionnaire, or to discover new ones.
Learn more in: Information Culture and Effective Use of Information Tools at Work: Conceptualizing and Measuring Group Adoption
9.
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
10.
( PCA): Used to find global feature from image /frame.
Learn more in: Digital Video Tampering Detection Techniques
11.
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
12.
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 component
s.
Learn more in: Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition
13.
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 component
s.
Learn more in: Measuring the Effects of Data Mining on Inference
14.
Principal component analysis
is a statistical approach that transforms a set of observations of possibly correlated variables into a set of linearly uncorrelated
principal component
s 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
15.
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 component
s.
Learn more in: Visualization Tools for Big Data Analytics in Quantitative Chemical Analysis: A Tutorial in Chemometrics
16.
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
17.
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
18.
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 component
s, 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
19.
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
Find more terms and definitions using our
Dictionary Search
.
Principal Component Analysis
appears in:
Handbook of Research on Text and Web Mining...
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