Lifelong multi-task multi-view learning using latent spaces

Abstract

In this paper, we study the problem of MTMV learning in a lifelong learning framework. Lifelong machine learning, like human lifelong learning, learns multiple tasks over time. Lifelong multi-task multi-view (Lifelong MTMV) learning is a new data mining and machine learning problem where new tasks and/or new views may come in anytime during the learning process. Our goal is to efficiently learn a model for a new task or new view by selectively transferring knowledge learned from previous tasks or views. To this end, we propose a latent space lifelong MTMV (lslMTMV) learning method to exploit task relatedness and information from multiple views. In this new method, we map views to a shared latent space and then learn a decision function in the latent space. Our new method supports knowledge sharing among multiple views and knowledge transfer from existing tasks to a new learning task naturally. We have evaluated our method using 3 real-world data sets. The experimental study results demonstrate that the classification accuracy of our algorithm is close or superior to state-of-the-art offline MTMV learning algorithms while the time needed to train such models is orders of magnitude less.