Weakly supervised nonnegative matrix factorization for user-driven clustering

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

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Clustering high-dimensional data and making sense out of its result is a challenging problem. In this paper, we present a weakly supervised nonnegative matrix factorization (NMF) and its symmetric version that take into account various prior information via regularization in clustering applications. Unlike many other existing methods, the proposed weakly supervised NMF methods provide interpretable and flexible outputs by directly incorporating various forms of prior information. Furthermore, the proposed methods maintain a comparable computational complexity to the standard NMF under an alternating nonnegativity-constrained least squares framework. By using real-world data, we conduct quantitative analyses to compare our methods against other semi-supervised clustering methods. We also present the use cases where the proposed methods lead to semantically meaningful and accurate clustering results by properly utilizing user-driven prior information.

Original languageEnglish
Pages (from-to)1598-1621
Number of pages24
JournalData Mining and Knowledge Discovery
Issue number6
Publication statusPublished - 2015 Nov 29


  • Nonnegative matrix factorization
  • Regularization
  • Semi-supervised clustering
  • User-driven clustering

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications


Dive into the research topics of 'Weakly supervised nonnegative matrix factorization for user-driven clustering'. Together they form a unique fingerprint.

Cite this