Non-negative Matrix Factorization for Binary Data

Jacob Søgaard Larsen, Line Katrine Harder Clemmensen

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 logistic matrix factorization without non-negativity constraint on the features. 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 termdocument 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.

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Paper Citation


in Harvard Style

Larsen J. and Clemmensen L. (2015). Non-negative Matrix Factorization for Binary Data . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: SSTM, (IC3K 2015) ISBN 978-989-758-158-8, pages 555-563. DOI: 10.5220/0005614805550563


in Bibtex Style

@conference{sstm15,
author={Jacob Søgaard Larsen and Line Katrine Harder Clemmensen},
title={Non-negative Matrix Factorization for Binary Data},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: SSTM, (IC3K 2015)},
year={2015},
pages={555-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005614805550563},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: SSTM, (IC3K 2015)
TI - Non-negative Matrix Factorization for Binary Data
SN - 978-989-758-158-8
AU - Larsen J.
AU - Clemmensen L.
PY - 2015
SP - 555
EP - 563
DO - 10.5220/0005614805550563