Cross-Domain Multitask Learning with Latent Probit Models

Han, S

Liao, X

Carin, L

2014-07-22T16:20:53Z

Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.

http://arxiv.org/abs/1206.6419v1

https://hdl.handle.net/10161/8955

icml.cc / Omnipress

cs.LG

cs.LG

stat.ML

Cross-Domain Multitask Learning with Latent Probit Models

Journal article

http://arxiv.org/abs/1206.6419v1

Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

Duke

Electrical and Computer Engineering

Pratt School of Engineering