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Sparse Principal Component Analysis Explained by emi is a document available to read on EtoBox.
This article discusses sparse principal component analysis (SPCA), a modification of principal component analysis (PCA) that aims to produce principal components with sparse loadings. The authors first formulate PCA as a regression optimization problem. They then impose lasso (elastic net) constraints on the regression coefficients to obtain sparse loadings. Efficient algorithms are proposed to fit SPCA models for multivariate data and gene expression data. SPCA is applied to real and simulated datasets wit
- Author
- emi
- Language
- EN