Non-negative Matrix Factorization for Binary Data

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

We propose the Logistic Non-negative Matrix Factorization for decomposition of binary data. Binary data are frequently generated in e.g. text analysis, sensory data, market basket data etc. A common method for analysing non-negative data is the Non-negative Matrix Factorization, though this is in theory not appropriate for binary data, and thus we propose a novel Non-negative Matrix Factorization based on the logistic link function. Furthermore we generalize the method to handle missing data. The formulation of the method is compared to a previously proposed method (Tome et al., 2015). We compare the performance of the Logistic Non-negative Matrix Factorization to Least Squares Non-negative Matrix Factorization and Kullback-Leibler (KL) Non-negative Matrix Factorization on sets of binary data: a synthetic dataset, a set of student comments on their professors collected in a binary term-document matrix and a sensory dataset. We find that choosing the number of components is an essential part in the modelling and interpretation, that is still unresolved.
Original languageEnglish
Publication date2015
Number of pages9
Publication statusPublished - 2015
Event7th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2015) - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015
Conference number: 7
http://www.kdir.ic3k.org/BooksPublished.aspx?y=2015

Conference

Conference7th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2015)
Number7
CountryPortugal
CityLisbon
Period12/11/201514/11/2015
Internet address

Keywords

  • Non-negative Matrix Factorization
  • Binary Data
  • Binary Matrix Factorization
  • Text Modelling

Cite this

Larsen, J. S., & Clemmensen, L. K. H. (2015). Non-negative Matrix Factorization for Binary Data. Paper presented at 7th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2015), Lisbon, Portugal.