Convex Factorization Machine for Toxicogenomics Prediction

Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang
2017 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17  
We introduce the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Speci cally, we employ a linear+quadratic model and regularize the linear term with the 2 -regularizer and the quadratic term with the trace norm regularizer. en, we formulate the CFM optimization as a semide nite programming problem and propose an e cient optimization procedure with Hazan's algorithm. A key advantage of CFM over existing FMs is that it can nd a
more » ... y optimal solution, while FMs may get a poor locally optimal solution since the objective function of FMs is non-convex. In addition, the proposed algorithm is simple yet e ective and can be implemented easily. Finally, CFM is a general factorization method and can also be used for other factorization problems, including multi-view matrix factorization and tensor completion problems, in various domains including toxicogenomics and bioinformatics. rough synthetic and traditionally used movielens datasets, we rst show that the proposed CFM achieves results competitive to FMs. We then show in a toxicogenomics prediction task that CFM predicts the toxic outcomes of a collection of drugs be er than a state-of-the-art tensor factorization method.
doi:10.1145/3097983.3098103 dblp:conf/kdd/YamadaLGCWKKMC17 fatcat:yl4rses5cbcyph5icjqyufmvku