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

Ava Bargi; Richard Yi Da Xu; Massimo Piccardi

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arXiv · 1307.0578

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

Ava Bargi·Richard Yi Da Xu·Massimo Piccardi

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Abstract

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.

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Ava Bargi, Richard Yi Da Xu, Massimo Piccardi. 2013-07-02. A non-parametric conditional factor regression model for high-dimensional input and response. https://arxiv.org/abs/1307.0578

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