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Abstract: Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and... (Update)
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...the E step, the expectation of Znj is E[znj Itr Oj] rnj (equation (1. 2) measured only on tr (the observed dimensions of 4 Following [5], we introduce t = E(tlzj = 1, t2,0j) y )Od. moo.o l(t (y)old) 1.3) which is the least squares regression between tn TM and tr...
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BibTeX entry: (Update)
Z. Ghahramani and M. I. Jordan, "Learning from Incomplete Data," by ftp to publications. ai.mit.edu. Massachusetts Institute of Technology, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Department of Brain and Cognitive Sciences, December 10, 1995. http://citeseer.ist.psu.edu/ghahramani95learning.html More
@techreport{ ghahramani94learning,
author = "Zoubin Ghahramani and Michael I. Jordan",
title = "Learning from Incomplete Data",
number = "AIM-1509",
pages = "11",
year = "1994",
url = "citeseer.ist.psu.edu/ghahramani95learning.html" }
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