Dimensionality reduction for binary data through the projection of natural parameters

DOI10.1016/j.jmva.2020.104668zbMath1450.62069arXiv1510.06112OpenAlexW2164214353MaRDI QIDQ2201560

Yoonkyung Lee, Andrew J. Landgraf

Publication date: 29 September 2020

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.06112

zbMATH Keywords

exponential familybinary datalogistic PCAprincipal component analysis (PCA)

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (4)

Maximum A Posteriori Inference of Random Dot Product Graphs via Conic Programming ⋮ Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models ⋮ Percolate: an exponential family JIVE model to design DNA-based predictors of drug response ⋮ Bayesian mean-parameterized nonnegative binary matrix factorization

Uses Software

• R
• LowRankModels
• Pyglrm

Cites Work

• Unnamed Item
• Unnamed Item
• A feasible method for optimization with orthogonality constraints
• Sparse logistic principal components analysis for binary data
• Principal component analysis of binary data by iterated singular value decomposition
• Principal component analysis.
• Generalized Low Rank Models
• Probabilistic Principal Component Analysis
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Optimization

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