Clustering of multivariate binary data with dimension reduction via \(L_{1}\)-regularized likelihood maximization

DOI10.1016/j.patcog.2015.05.026zbMath1394.68322OpenAlexW582033862MaRDI QIDQ1669628

Kenichi Hayashi, Michio Yamamoto

Publication date: 3 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2015.05.026

zbMATH Keywords

EM algorithmclusteringdimension reductionsparsitybinary datalatent class analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Dimension reduction for longitudinal multivariate data by optimizing class separation of projected latent Markov models, Efficient mixture model for clustering of sparse high dimensional binary data, Simultaneous method of orthogonal non-metric non-negative matrix factorization and constrained non-hierarchical clustering

Uses Software

• UCI-ml

Cites Work

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