principal components analysis sentence in Hindi
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- This 50-dimensional space is flattened into a plane using principal components analysis ( PCA ).
- The simplest approach, however, is to perform a standard principal component analysis ( PCA ).
- It is conceptually similar to principal component analysis, but applies to categorical rather than continuous data.
- The higher dimensionality of the descriptor is reduced to 64 through principal components analysis ( PCA ).
- The indexes have been created by the Australian Bureau of Statistics, the national principal component analysis.
- Factor analysis is related to principal component analysis ( PCA ), but the two are not identical.
- Examples are principal component analysis and discriminant analysis, and the quartimax and oblimax methods in factor analysis.
- Factor analysis is similar to principal component analysis, in that factor analysis also involves linear combinations of variables.
- More generally, principal component analysis can be used as a method of factor analysis in structural equation modeling.
- Such vectors are sometimes called the Principal Component Analysis, the basis vectors are not required to be orthogonal.
- For a second analysis, the scores from both cultures were combined, and principal components analysis was used.
- This is to be contrasted with principal component analysis which seeks to minimize the mean square error of all residuals.
- Face images usually occupy a high-dimensional space and conventional principal component analysis was intractable on such data sets.
- For each area, it learns a separate Principal Component Analysis ( PCA ) basis and reconstructs the area separately.
- This problem is a shortcoming of principal component analysis in general, not just of M-SSA in particular.
- Ordinary principal component analysis ( PCA ) uses a vector space transform to reduce multidimensional data sets to lower dimensions.
- By applying principal component analysis to data from " classical genetic markers " ( protein cline with a Near Eastern focus.
- Since centered data is required to perform an effective principal component analysis, we'centralize'K to become K'
- These are then reduced to 10-15 dimensions by principal component analysis, giving the appearance information A \,.
- Random Project does not perform as well as Principal Component Analysis at preserving inter-point distances but is computationally cheaper.
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