Sparse Principal Component Analysis With Preserved Sparsity Pattern
Sparse Principal Component Analysis With Preserved Sparsity Pattern is a scholarly work by Inge Koch and Abd-Krim Seghouane, published in 2019 in ''IEEE Transactions on Image Processing''. The main subjects of the publication include sparse matrix, independent component analysis, mathematics, pattern recognition, Sparse PCA, artificial intelligence, principal component analysis, compressed sensing, feature, feature extraction, feature, graph dimension, computer science, chemometrics, Sparse modeling, and dimensionality reduction. The authors further demonstrate that the authors' proposed sparse PCA method can be used to improve the performance of blind source separation for functional magnetic resonance imaging data.