Weakly supervised nonnegative matrix factorization for user-driven clustering

DOI10.1007/s10618-014-0384-8zbMath1405.62075OpenAlexW2150594776WikidataQ58312545 ScholiaQ58312545MaRDI QIDQ1715911

Jaegul Choo, Haesun Park, Chandan K. Reddy, Changhyun Lee

Publication date: 29 January 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-014-0384-8

zbMATH Keywords

regularizationnonnegative matrix factorizationsemi-supervised clusteringuser-driven clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Factorization of matrices (15A23)

Uses Software

• SymNMF

Cites Work

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• Semi-supervised graph clustering: a kernel approach
• SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering
• Document clustering using nonnegative matrix factorization
• Fast Nonnegative Matrix Factorization: An Active-Set-Like Method and Comparisons
• 10.1162/jmlr.2003.3.4-5.993
• Learning the parts of objects by non-negative matrix factorization
• Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
• Projected Gradient Methods for Nonnegative Matrix Factorization

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