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Book synopsis

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.

Book details

Book category Books in English Computing & information technology Computer programming / software development Programming & scripting languages: general

• Full title: Missing Data Problems in Machine Learning
• Subtitle: Outline and Contributions
• Author: Robin Parker
• Language: English
• Binding: Paperback
• Number of pages: 168
• EAN: 9783639212280
• ISBN: 3639212282
• ID: 06829144
• Publisher: VDM Verlag
• Weight: 254 g
• Dimensions: 229 × 152 × 10 mm
• Date of publishing: 07. June 2010

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