A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response


Ava Bargi, Richard Yi Xu, Zoubin Ghahramani, Massimo Piccardi ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:77-85, 2014.


In this paper, we propose a non-parametric conditional factor regression (NCFR) model for domains with multi-dimensional input and response. NCFR enhances linear regression in two ways: a) introducing low-dimensional latent factors leading to dimensionality reduction and b) integrating the Indian Buffet Process as prior for the latent layer to dynamically derive an optimal number of sparse factors. Thanks to IBP’s enhancements to the latent factors, NCFR can significantly avoid over-fitting even in the case of a very small sample size compared to the dimensionality. Experimental results on three diverse datasets comparing NCRF to a few baseline alternatives give evidence of its robust learning, remarkable predictive performance, good mixing and computational efficiency.

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