Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras
Ioannis Kordonis Emmanouil Theodosis George Retsinas Petros Maragos
Matrix Factorization (MF) has found numerous applications in Machine Learning
and Data Mining, including collaborative filtering recommendation systems,
dimensionality reduction, data visualization, and community detection.
Motivated by the recent successes of tropical algebra and geometry in machine
learning, we investigate two problems involving matrix factorization over the
tropical algebra. For the first problem, Tropical Matrix Factorization (TMF),
which has been studied already in the literature, we propose an improved
algorithm that avoids many of the local optima. The second formulation
considers the approximate decomposition of a given matrix into the product of
three matrices where a usual matrix product is followed by a tropical product.
This formulation has a very interesting interpretation in terms of the learning
of the utility functions of multiple users. We also present numerical results
illustrating the effectiveness of the proposed algorithms, as well as an
application to recommendation systems with promising results.