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Technological innovations R－6 High accuracy classification and regression using relations between features Learning from huge number of feature combinations with Convex Factorization Machines In many classification or regression tasks, it is possible to improve accuracy by taking into account the latent relationships between features. We developed a new technology based on convex optimization called “Convex Factorization Machines” (Convex FM), which can handle feature combinations efficiently, even when the number of features is very large. Example of recommender system using Convex FM Movie B Alice ☆☆☆ ☆ Bob ☆☆☆ ☆☆ Charlie ☆☆☆ ? User Age Movie Genre Alice 19 Movie A Fantasy Bob 47 Movie B Adventure Charlie 50 Convert to regression problem … X … Movie A … Movie data Features Rating User Movie Age Genre Alice Movie A 19 Fantasy Alice Movie B 19 Adventure ☆ Bob Movie B 47 Fantasy ☆☆☆ Bob Movie B 47 Adventure ☆☆ y User Movie Age Genre Charlie Movie B 50 Adventure User × Movie Movie × Age User × Genre … ■ Relational data analysis can be reduced to a regression problem, in which auxiliary data can be easily incorporated. ■ Since the proposed method is based on convex optimization, parameter estimation is insensitive to initialization and therefore model training is easy to use. ■ Recommender systems that use user or product auxiliary data. ■ Finding interactions between genes that cause diseases. ■ Predicting rice yield from interactions between rice genes. Convex FM Feature vector to predict: ■ Because it is possible to infer combinations that were not observed in the training set, we can discover new knowledge. Application Scenarios … … Training ☆☆☆ ■ Predictive models can be efficiently trained even when the number of features and combinations is very large. Rating ☆☆ Convex FM can be trained efficiently even when the number of combinations is very large! 〈Contact〉[email protected] Copyright © 2016 NTT. All Rights Reserved.
