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In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called<jats:italic>Sparse Tropical Matrix Factorization<\/jats:italic>() for the estimation of missing (unknown) values in sparse data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluate the efficiency of the 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 approximation achieves a higher correlation than non-negative matrix factorization (), which is unable to recover patterns effectively. On real data, outperforms on six out of nine gene expression datasets. While assumes normal distribution and tends toward the mean value, can better fit to extreme values and distributions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>is the first work that uses tropical semiring on sparse data. 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