A non-parametric conditional factor regression model for high-dimensional input and response
Machine Learning
2013-07-03 v1 Machine Learning
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.
Cite
@article{arxiv.1307.0578,
title = {A non-parametric conditional factor regression model for high-dimensional input and response},
author = {Ava Bargi and Richard Yi Da Xu and Massimo Piccardi},
journal= {arXiv preprint arXiv:1307.0578},
year = {2013}
}
Comments
9 pages, 3 figures, NIPS submission