Multi-Learning Generalised Low-Rank Models

Abstract

Multi-output supervised learning and multi-task learning are all instances of a broader learning paradigm where features, parameters and objectives are shared to a certain extent. Examples of such approaches include reusing features from pre-existing models in a new algorithm, performing multi-label regression or optimising for several tasks jointly. In this paper, we address this challenge by devising a generic framework based on generalised low-rank models ("GLRMs"), which include – broadly speaking– most techniques that can be expressed in terms of matrix factorisation. Importantly, while GLRMs first and foremost tackle unsupervised learning problems and supervised linear models. Here, we show that GLRMs can be extended by introducing multivariate functionals and structure regularisation terms to handle multivariate learning. This paper also proposes a coherent framework to design multi-learning strategies and covers existing algorithms. Finally, we prove the simplicity and effectiveness of our approach on empirical data.