CUED Publications database

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

Bargi, A and Da Xu, RY and Ghahramani, Z and Piccardi, M (2014) A non-parametric conditional factor regression model for multi-dimensional input and response. In: UNSPECIFIED pp. 77-85..

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:59
Last Modified: 03 Aug 2017 03:14
DOI: