Examples of using Principal component analysis in English and their translations into Hungarian
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Computer
What is Principal Component Analysis?
Supervised Machine Learning- Dimensional Reduction and Principal Component Analysis.
Principal component analysis for emotion recognition.
Exactly what is Principal Component Analysis?
Principal component analysis is sensitive to the relative scaling of the originally used variables.
Dimensionality reduction with principal component analysis. .
Principal component analysis employs a mathematical transformation to the original data with no assumptions about the form of the covariance matrix.
In most cases we use Principal Component Analysis(PCA).
In this section, from among other things,we provide a brief introduction to Support Vector Machine(SVM), and Principal Component Analysis(PCA).
The initial extraction using principal component analysis revealed 5 components with eigenvalues above 1.
Examples thereof are Support Vector Machines(SVM) and Kernel PCA(Principal Component Analysis).
In my thesis I also worked on the implementation of principal component analysis, but it was not successful because of the data representation method.
However, a large number of variables are available in the case of disharmonies within the family,I first carried out a principal component analysis.
Also includes Cluster analysis and Principal Component analysis modules-.
Table 17 shows the component matrix derived from Alexa Rank, Google PageRank,and Yahoo links indicators with principal component analysis without rotation.
With this knowledge I will attempt a new procedure, the principal component analysis which is used to determine which acoustic parameters are relevant to emotion recognition.
The linear combinations obtained using Fisher's linear discriminant are called Fisher faces,while those obtained using the related principal component analysis are called eigenfaces.
Dr Zharkova andher colleagues derived their model using a technique called'principal component analysis' of the magnetic field observations from the Wilcox Solar Observatory in California.
The biggest part of my work deals with description of the basis of principal component analysis and it's practical implementation.
Recall that in principal components analysis( Principal Component Analysis(PCA)) you look for the combination of the original variables that has the largest possible variation.
Standard principal components analysis assumes linear relationships between numeric variables.
Factor analysis is mathematically more complex than Principal Components Analysis.
Exploratory factor analysis and principal components analysis.
From top to bottom: Sentinel-2 natural colour composite,Sentinel-2 principal components analysis, Shuttle Radar Topography Mission-based elevation and the resulting geological map at 1:50000 scale.(courtesy: GAF).
Principal Component and factor analysis.
Principal Component and Factor Analyses.
Like Principal components analysis and independent components analysis. .
For nonlinear molecules which are symmetric rotors(or symmetric tops- see next section), there are two moments of inertia and the energy also depends on a second rotational quantum number, K{\displaystyle K},which defines the vector component of rotational angular momentum along the principal symmetry axis.[6] Analysis of spectroscopic data with the expressions detailed below results in quantitative determination of the value(s) of the moment(s) of inertia. From these precise values of the molecular structure and dimensions may be obtained.
Principal Components and Factor analysis.
These included principal components factor analyses, with rotated varimax factors.