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- Sparse_PCA abstract "Sparse principal component analysis (sparse PCA) is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate data sets. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by adding sparsity constraint on the input variables.Ordinary principal component analysis (PCA) uses a vector space transform to reduce multidimensional data sets to lower dimensions. It finds linear combinations of input variables, and transforms them into new variables (called principal components) that correspond to directions of maximal variance in the data. The number of new variables created by these linear combinations is usually much lower than the number of input variables in the original dataset, while still explaining most of the variance present in the data.A particular disadvantage of ordinary PCA is that the principal components are usually linear combinations of all input variables. Sparse PCA overcomes this disadvantage by finding linear combinations that contain just a few input variables.".
- Sparse_PCA wikiPageID "18566488".
- Sparse_PCA wikiPageLength "12052".
- Sparse_PCA wikiPageOutDegree "23".
- Sparse_PCA wikiPageRevisionID "700623172".
- Sparse_PCA wikiPageWikiLink Category:Machine_learning_algorithms.
- Sparse_PCA wikiPageWikiLink Category:Multivariate_statistics.
- Sparse_PCA wikiPageWikiLink Consistency_(statistics).
- Sparse_PCA wikiPageWikiLink Convex_set.
- Sparse_PCA wikiPageWikiLink Covariance_matrix.
- Sparse_PCA wikiPageWikiLink Eigenvalues_and_eigenvectors.
- Sparse_PCA wikiPageWikiLink Linear_combination.
- Sparse_PCA wikiPageWikiLink List_of_transforms.
- Sparse_PCA wikiPageWikiLink Lp_space.
- Sparse_PCA wikiPageWikiLink Matrix_(mathematics).
- Sparse_PCA wikiPageWikiLink Multivariate_analysis.
- Sparse_PCA wikiPageWikiLink NP-hardness.
- Sparse_PCA wikiPageWikiLink Orthogonality.
- Sparse_PCA wikiPageWikiLink Planted_clique.
- Sparse_PCA wikiPageWikiLink Positive-definite_matrix.
- Sparse_PCA wikiPageWikiLink Principal_component_analysis.
- Sparse_PCA wikiPageWikiLink Rank_(linear_algebra).
- Sparse_PCA wikiPageWikiLink Scikit-learn.
- Sparse_PCA wikiPageWikiLink Semidefinite_programming.
- Sparse_PCA wikiPageWikiLink Trace_(linear_algebra).
- Sparse_PCA wikiPageWikiLink Variance.
- Sparse_PCA wikiPageWikiLink Vector_space.
- Sparse_PCA wikiPageWikiLinkText "Sparse PCA".
- Sparse_PCA wikiPageUsesTemplate Template:EquationNote.
- Sparse_PCA wikiPageUsesTemplate Template:EquationRef.
- Sparse_PCA subject Category:Machine_learning_algorithms.
- Sparse_PCA subject Category:Multivariate_statistics.
- Sparse_PCA hypernym Technique.
- Sparse_PCA type TopicalConcept.
- Sparse_PCA type Type.
- Sparse_PCA type Algorithm.
- Sparse_PCA type Type.
- Sparse_PCA comment "Sparse principal component analysis (sparse PCA) is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate data sets. It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by adding sparsity constraint on the input variables.Ordinary principal component analysis (PCA) uses a vector space transform to reduce multidimensional data sets to lower dimensions.".
- Sparse_PCA label "Sparse PCA".
- Sparse_PCA sameAs Q7573786.
- Sparse_PCA sameAs m.04g1dc6.
- Sparse_PCA sameAs Q7573786.
- Sparse_PCA wasDerivedFrom Sparse_PCA?oldid=700623172.
- Sparse_PCA isPrimaryTopicOf Sparse_PCA.