\(\mathrm{C_{enet}Biplot}\): a new proposal of sparse and orthogonal biplots methods by means of elastic net CSVD
DOI10.1007/S11634-021-00468-1OpenAlexW3215191464MaRDI QIDQ6106142
Purificación Galindo Villardón, Ana Belén Nieto-Librero, Nerea González-García
Publication date: 27 June 2023
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11634-021-00468-1
Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Factorization of matrices (15A23) Multivariate analysis (62Hxx)
Cites Work
- Unnamed Item
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- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
- CUR matrix decompositions for improved data analysis
- Sparse principal component analysis via regularized low rank matrix approximation
- Clustering and disjoint principal component analysis
- From simple structure to sparse components: a review
- Intelligent computing theories and technology. 9th international conference, ICIC 2013, Nanning, China, July 28--31, 2013. Proceedings
- Weakly Correlated Sparse Components with Nearly Orthonormal Loadings
- Generalized power method for sparse principal component analysis
- ggplot2
- Principal component analysis: a review and recent developments
- Regularization and Variable Selection Via the Elastic Net
- The biplot graphic display of matrices with application to principal component analysis
- Convex analysis and monotone operator theory in Hilbert spaces
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