Guided Projections for Analyzing the Structure of High-Dimensional Data
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Publication:3391156
DOI10.1080/10618600.2018.1459304OpenAlexW2598695409MaRDI QIDQ3391156
Sarka Brodinova, Thomas Ortner, Peter Filzmoser, Maia Rohm, Christian Breiteneder
Publication date: 28 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2018.1459304
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- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
- Fast and robust discriminant analysis
- Outlier identification in high dimensions
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Extracting informative variables in the validation of two-group causal relationship
- Principal manifolds for data visualization and dimension reduction. Reviews and original papers presented partially at the workshop `Principal manifolds for data cartography and dimension reduction', Leicester, UK, August 24--26, 2006.
- Diffusion maps
- Measuring the Power of Hierarchical Cluster Analysis
- QUADRATIC ASSIGNMENT AS A GENERAL DATA ANALYSIS STRATEGY
- A Projection Pursuit Algorithm for Exploratory Data Analysis
- 10.1162/153244303322753616
- Regularization and Variable Selection Via the Elastic Net