- Tytuł:
- Exponential machines
- Autorzy:
-
Novikov, A.
Trofimov, M.
Oseledets, I. - Powiązania:
- https://bibliotekanauki.pl/articles/201849.pdf
- Data publikacji:
- 2018
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
tensor decomposition
tensor train
factorization machines
Riemannian optimization
optymalizacja
dekompozycja tensorowa
tensor metryczny Riemanna - Opis:
- Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called tensor train (TT). The tensor train format regularizes the model and lets you control the number of underlying parameters. To train the model, we develop a stochastic Riemannian optimization procedure, which allows us to fit tensors with ¼ 256 entries. We show that the model achieves state-of-the-art performance on synthetic data with high-order interactions and that it works on par with high-order factorization machines on a recommender system dataset MovieLens 100 K.
- Źródło:
-
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 789-797
0239-7528 - Pojawia się w:
- Bulletin of the Polish Academy of Sciences. Technical Sciences
- Dostawca treści:
- Biblioteka Nauki
Artykuł