Learning Discriminative Metrics via Generative Models and Kernel Learning by Yuan Shi, Yung-Kyun Noh, Fei Sha, and Daniel D. Lee.
Abstract:
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. These are then used as base kernels to construct a global kernel that minimizes a discriminative training criterion. We consider both linear and nonlinear combinations of local metric kernels. Our empirical results show that these combinations significantly improve performance on classification tasks. The proposed learning algorithm is also very efficient, achieving order of magnitude speedup in training time compared to previous discriminative baseline methods.
Combination of machine learning techniques within a framework.
It may be some bias in my reading patterns but I don’t recall any explicit combination of human + machine learning techniques? I don’t take analysis of search logs to be an explicit human contribution since the analysis is guessing as to why a particular link and not another was chosen. I suppose time on the resource chosen might be an indication but a search log per se isn’t going to give that level of detail.
For that level of detail you would need browsing history. Would be interesting to see if a research library or perhaps employer (fewer “consent” issues) would permit browsing history collection over some long period of time, say 3 to 6 months. So that not only is the search log captured but the entire browsing history.
Hard to say if that would result in enough increased accuracy on search results to be worth the trouble.
Interesting paper about combining purely machine learning techniques and promises significant gains. What these plus human learning would produce remains a subject for future research papers.