Automatic Regularization of Factorization Models

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Many recent machine learning approaches for prediction problems over categorical variables are based on factorization models, e.g. matrix or tensor factorization. Due to the large number of model parameters, factorization models are prone to overfitting and typically Gaussian priors are applied for regularization. Finding proper values for the regularization parameters is usually done with an expensive grid-search using holdout validation data. In this work, two approaches are presented where regularization values are found without increasing computational complexity. The first one is based on interweaving optimization of model parameters and regularization in stochastic gradient descent algorithms. Secondly, a two-level Bayesian model to integrate regularization values into inference is shortly discussed.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of KonstanzKonstanzGermany

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