Block-diagonal approach to non-negative factorization of sparse linguistic matrices and tensors of extra-large dimension using the latent Dirichlet distribution

DOI10.1007/S10559-018-0087-ZzbMath1486.68193OpenAlexW2901006483WikidataQ114227146 ScholiaQ114227146MaRDI QIDQ1735306

O. O. Marchenko, E. Ì. Nasirov, Anatoly V. Anisimov

Publication date: 28 March 2019

Published in: Cybernetics and Systems Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10559-018-0087-z

zbMATH Keywords

parallel computationartificial intelligencecomputational linguistics

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Factorization of matrices (15A23) Parallel numerical computation (65Y05) Natural language processing (68T50)

Uses Software

• TensorToolbox

Cites Work

• A method for automatic construction of ontological knowledge bases. I: Development of a semantic-syntactic model of natural language
• Document clustering using nonnegative matrix factorization
• On the Complexity of Nonnegative Matrix Factorization
• 10.1162/jmlr.2003.3.4-5.993

This page was built for publication: Block-diagonal approach to non-negative factorization of sparse linguistic matrices and tensors of extra-large dimension using the latent Dirichlet distribution