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Sparse PCA - Wikipedia

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Sparse principal component analysis (SPCA or sparse PCA) is a 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 introducing sparsity structures to the input variables. A particular disadvantage of ordinary PCA is that the principal components are usually linear combinations of all input variables. SPCA overcomes this disadvantage by finding components that are linear combinations of just a few input variables (SPCs). This means that some of the coefficients of the linear combinations defining the SPCs, called loadings,[note 1] are equal to zero. The number of nonzero loadings is called the cardinality of the SPC. Consider a data matrix, 𝑋 , where each of the 𝑝 columns represent an input variable, and each of the 𝑛 rows represents an independent sample from data population. One assumes each column of 𝑋 has mean

Sparse PCA - Wikipedia Jump to content From Wikipedia, the free encyclopedia Statistical analysis technique Sparse principal component analysis (SPCA or sparse PCA) is a 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 introducing sparsity structures to the input variables. A particular disadvantage of ordinary PCA is that the principal components are usually linear combinations of all input variables. SPCA overcomes this disadv

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