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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

13 Sitaatiot (Scopus)

Abstrakti

We introduce the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs). Specifically, we employ a linear+quadratic model and regularize the linear term with the l2-regularizer and the quadratic term with the trace norm regularizer. Then, we formulate the CFM optimization as a semidefinite programming problem and propose an efficient optimization procedure with Hazan's algorithm. A key advantage of CFM over existing FMs is that it can find a globally 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 effective 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. Through synthetic and traditionally used movielens datasets, we first 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 better than a state-of-the-art tensor factorization method.

AlkuperäiskieliEnglanti
OtsikkoKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
KustantajaACM
Sivut1215-1224
Sivumäärä10
ISBN (elektroninen)9781450348874
DOI - pysyväislinkit
TilaJulkaistu - 13 elokuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Halifax, Kanada
Kesto: 13 elokuuta 201717 elokuuta 2017
Konferenssinumero: 23

Conference

ConferenceACM SIGKDD International Conference on Knowledge Discovery and Data Mining
LyhennettäKDD
MaaKanada
KaupunkiHalifax
Ajanjakso13/08/201717/08/2017

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