Missing Data Problems in Machine Learning (Paperback) (ISBN-13: 9783639212280)

Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with non-random missing data and classification with missing features. We begin by presenting and elaborating on the theory of missing data due to Little and Rubin. We place a particular emphasis on the missing at random assumption in the multivariate setting with arbitrary patterns of missing data. We derive inference and prediction methods in the presence of random missing data for a variety of probabilistic models including finite mixture models, Dirichlet process mixture models, and factor analysis.

Author: Robin Parker
ISBN-10: 3639212282
ISBN-13: 9783639212280
Publisher: VDM Verlag
Language: English
Published: 06/07/2010
Pages: 168
Format: Paperback
Weight: 0.56lbs
Size: 9.00h x 6.00w x 0.39d