Learning with Target Prior
Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji · 2012
In the conventional approaches for supervised parametric learning, relations be-tween data and target variables are provided through training sets consisting of pairs of corresponded data and target variables. In this work, we describe a new learning scheme for parametric learning, in which the target variables y can be modeled with a prior model p(y) and the relations between data and target variables are estimated with p(y) and a set of uncorresponded data X in train-ing. We term this method as learning with target priors (LTP). Specifically, LTP learning seeks parameter θ that maximizes the log likelihood of fθ(X) on a uncorresponded training set with regards to p(y). Compared to the conventional (semi)supervised learning approach, LTP can make efficient use of prior knowl-edge of the target variables in the form of probabilistic distributions, and thus re-moves/reduces the reliance on training data in learning. Compared to the Bayesian approach, the learned parametric regressor in LTP can be more efficiently imple-mented and deployed in tasks where running efficiency is critical. We demonstrate the effectiveness of the proposed approach on parametric regression tasks for BCI signal decoding and pose estimation from video. 1