Latent Composite Likelihood Learning for the Structured Canonical Correlation Model.
Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Eighth Conference (2012).
(pp. pp. 765-774).
AUAI Press: Corvallis, Oregon, USA.
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Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inflation" in economics). This leads to the development of measurement instruments to record noisy indirect evidence for such unobserved variables such as surveys, tests and price indexes. In such problems, there are postulated latent variables and a given measurement model. At the same time, other unantecipated latent variables can add further unmeasured confounding to the observed variables. The problem is how to deal with unantecipated latents variables. In this paper, we provide a method loosely inspired by canonical correlation that makes use of background information concerning the "known" latent variables. Given a partially specified structure, it provides a structure learning approach to detect "unknown unknowns," the confounding effect of potentially infinitely many other latent variables. This is done without explicitly modeling such extra latent factors. Because of the special structure of the problem, we are able to exploit a new variation of composite likelihood fitting to efficiently learn this structure. Validation is provided with experiments in synthetic data and the analysis of a large survey done with a sample of over 100,000 staff members of the National Health Service of the United Kingdom.
|Title:||Latent Composite Likelihood Learning for the Structured Canonical Correlation Model|
|Event:||26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)|
|Open access status:||An open access version is available from UCL Discovery|
|Additional information:||Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012). Copyright © 2012 by AUAI Press.|
|Keywords:||stat.ML, stat.ML, cs.LG|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
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