# Beyond Trees: Classification with Sparse Pairwise Dependencies

@article{Tenzer2020BeyondTC, title={Beyond Trees: Classification with Sparse Pairwise Dependencies}, author={Yaniv Tenzer and Amit Moscovich and Mary Frances Dorn and Boaz Nadler and Clifford H. Spiegelman}, journal={J. Mach. Learn. Res.}, year={2020}, volume={21}, pages={189:1-189:33} }

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise…

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