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- Sparse_PCA abstract "Sparse PCA (sparse principal component analysis) is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate data sets.Ordinary principal component analysis (PCA) uses a vector space transform used to reduce multidimensional data sets to lower dimensions for analysis. It finds linear combinations of 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 variables in the original dataset. Sparse PCA finds sets of sparse vectors for use as weights in the linear combinations while still explaining most of the variance present in the data.Several approaches have been proposed, including a regression framework, a convex relaxation/semidefinite programming framework, a generalized power method framework an alternating maximization framework forward/backward greedy search and exact methods using branch-and-bound techniques, Bayesian formulation framework.".
- Sparse_PCA wikiPageID "18566488".
- Sparse_PCA wikiPageRevisionID "567244014".
- Sparse_PCA hasPhotoCollection Sparse_PCA.
- Sparse_PCA subject Category:Machine_learning_algorithms.
- Sparse_PCA subject Category:Multivariate_statistics.
- Sparse_PCA comment "Sparse PCA (sparse principal component analysis) is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate data sets.Ordinary principal component analysis (PCA) uses a vector space transform used to reduce multidimensional data sets to lower dimensions for analysis. It finds linear combinations of variables (called "principal components") that correspond to directions of maximal variance in the data.".
- Sparse_PCA label "Sparse PCA".
- Sparse_PCA sameAs m.04g1dc6.
- Sparse_PCA sameAs Q7573786.
- Sparse_PCA sameAs Q7573786.
- Sparse_PCA wasDerivedFrom Sparse_PCA?oldid=567244014.
- Sparse_PCA isPrimaryTopicOf Sparse_PCA.