An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views
Ann Clifton, Max Whitney, Anoop Sarkar · 2013
We introduce an online framework for dis-criminative learning problems over hidden structures, where we learn both the latent structure and the classifier for a supervised learning task. Previous work on lever-aging latent representations for discrimi-native learners has used batch algorithms that require multiple passes though the en-tire training data. Instead, we propose an online algorithm that efficiently jointly learns the latent structures and the classi-fier. We further extend this to include mul-tiple views on the latent structures with different representations. Our proposed online algorithm with multiple views sig-nificantly outperforms batch learning for latent representations with a single view on a grammaticality prediction task. 1