  In this paper, we propose a non-parametric conditional factor regression
(NCFR)model for domains with high-dimensional input and response. NCFR enhances
linear regression in two ways: a) introducing low-dimensional latent factors
leading to dimensionality reduction and b) integrating an Indian Buffet Process
as a prior for the latent factors to derive unlimited sparse dimensions.
Experimental results comparing NCRF to several alternatives give evidence to
remarkable prediction performance.
