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

A non-parametric conditional factor regression model for high-dimensional input and response