Learning a Product of Experts with Elitist Lasso
Mengqiu Wang, Christopher D. Manning · 2013
Discriminative models such as logistic regression profit from the ability to incorporate arbitrary rich features; however, complex dependencies among overlapping features can often result in weight undertraining. One popular method that attempts to mitigate this problem is logarithmic opinion pools (LOP), which is a specialized form of product of experts model that automatically adjusts the weighting among experts. A major problem with LOP is that it requires significant amounts of domain expertise in designing effective experts. We propose a novel method that learns to induce experts — not just the weighting between them — through the use of a mixed ℓ2ℓ1 norm as previously seen in elitist lasso. Unlike its more popular sibling ℓ1ℓ2 norm (used in group lasso), which seeks feature sparsity at the group-level, ℓ2ℓ1 norm encourages sparsity within feature groups. We demonstrate how this property can be leveraged as a competition mechanism to induce groups of diverse experts, and introduce a new formulation of elitist lasso Max-Ent in the FOBOS optimization framework (Duchi and Singer, 2009). Results on Named Entity Recognition task suggest that this method gives consistent improvements over a standard logistic regression model, and is more effective than conventional induction schemes for experts. 1