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An overview of Frank-Wolfe optimization for stochasticity constrained interpretable matrix and tensor factorization

 
: Sifa, R.

:

Kůrková, V.:
Artificial Neural Networks and Machine Learning - ICANN 2018. Proceedings, Part II : 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11140)
ISBN: 978-3-030-01421-6
ISBN: 978-3-030-01420-9
ISBN: 978-3-030-01422-3
S.369-379
International Conference on Artificial Neural Networks (ICANN) <27, 2018, Rhodes>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
In this paper we give an overview about utilizing Frank Wolfe optimization to find interpretable constrained matrix and tensor factorizations. We will particularly concentrate on imposing stochasticity constraints and show how factors of Archetypal Analysis as well as Decomposition Into Directed Components can be found using Frank Wolfe optimization to respectively decompose bipartite matrices and asymmetric similarity tensors. We will show how the derived algorithms perform by presenting case studies from behavioral profiling in digital games.

: http://publica.fraunhofer.de/dokumente/N-515964.html