Max-margin Classification of Data with Absent Features

We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between the features. Unlike the case where a feature exists but its value is not observed, here we focus on the case where a feature may not even exist (structurally absent) for some of the samples. The common approach for handling missing features in discriminative models is to first complete their unknown values, and then use a standard classification procedure over the completed data. This paper focuses on features that are known to be non-existing, rather than have an unknown value. We show how incomplete data can be classified directly without any completion of the missing features using a max-margin learning framework. We formulate an objective function, based on the geometric interpretation of the margin, that aims to maximize the margin of each sample in its own relevant subspace. In this formulation, the l...

• Type: Journal
• Year: 2008
• Where: JMLR
• Authors: Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbeel, Daphne Koller
• Keywords: JMLR 2008, Missing Values, Standard Classification Procedure, Unknown Value