Sparse data embedding and prediction by tropical matrix factorization.

ABSTRACT:

Background

Matrix factorization methods are linear models, with limited capability to model complex relations. In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called Sparse Tropical Matrix Factorization (STMF) for the estimation of missing (unknown) values in sparse data.

Results

We evaluate the efficiency of the STMF method on both synthetic data and biological data in the form of gene expression measurements downloaded from The Cancer Genome Atlas (TCGA) database. Tests on unique synthetic data showed that STMF approximation achieves a higher correlation than non-negative matrix factorization (NMF), which is unable to recover patterns effectively. On real data, STMF outperforms NMF on six out of nine ge

SUBMITTER: Omanovic A 

PROVIDER: S-EPMC7908717 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature