Sparsest factor analysis for clustering variables: a matrix decomposition approach

DOI10.1007/s11634-017-0284-zzbMath1416.62319OpenAlexW2605632622MaRDI QIDQ1630876

Nickolay T. Trendafilov, Kohei Adachi

Publication date: 5 December 2018

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: http://oro.open.ac.uk/49278/1/ADAC-D-15-00148-R32_ORO.pdf

zbMATH Keywords

exploratory factor analysisvariable clusteringmatrix decomposition factor analysisQR re-parameterizationsparsest loadings

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Factorization of matrices (15A23)

Related Items

High-dimensional disjoint factor analysis with its EM algorithm version ⋮ Second-order disjoint factor analysis ⋮ Hierarchical disjoint principal component analysis ⋮ Principal component analysis constrained by layered simple structures ⋮ In the pursuit of sparseness: a new rank-preserving penalty for a finite mixture of factor analyzers

Uses Software

• sparsenet

Cites Work

• Sparse estimation via nonconcave penalized likelihood in factor analysis model
• Nonparametric Bayesian sparse factor models with application to gene expression modeling
• Exploratory factor and principal component analyses: some new aspects
• A generalization of Kristof's theorem on the trace of certain matrix products
• Clustering and disjoint principal component analysis
• Estimation of an oblique structure via penalized likelihood factor analysis
• A new method for simultaneous estimation of the factor model parameters, factor scores, and unique parts
• From simple structure to sparse components: a review
• Applied Factor Analysis in the Natural Sciences
• SparseNet: Coordinate Descent With Nonconvex Penalties
• <b>SOME CONTRIBUTIONS TO DATA-FITTING FACTOR </b><b>ANALYSIS WITH EMPIRICAL COMPARISONS TO </b><b>COVARIANCE-FITTING FACTOR ANALYSIS </b>
• Modern Multivariate Statistical Techniques
• Data Mining
• Data Clustering: Theory, Algorithms, and Applications
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