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Learning from Multiple Partially Observed Views -- an Application to Multilingual Text Categorization

Abstract : We address the problem of learning classifiers when observations have multiple views, some of which may not be observed for all examples. We assume the existence of view generating functions which may complete the missing views in an approximate way. This situation corresponds for example to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems may be used to translate each document in the missing languages. We derive a generalization error bound for classifiers learned on examples with multiple artificially created views. Our result uncovers a trade-off between the size of the training set, the number of views, and the quality of the view generating functions. As a consequence, we identify situations where it is more interesting to use multiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semi-supervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 collections support our findings by showing that additional views obtained from MT may significantly improve the classification performance in the cases identified by our trade-off.
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https://hal.archives-ouvertes.fr/hal-01297947
Contributor : Lip6 Publications <>
Submitted on : Tuesday, April 5, 2016 - 10:56:31 AM
Last modification on : Thursday, March 21, 2019 - 1:11:47 PM

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  • HAL Id : hal-01297947, version 1

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Massih-Reza Amini, Nicolas Usunier, Cyril Goutte. Learning from Multiple Partially Observed Views -- an Application to Multilingual Text Categorization. Advances in Neural Information Processing Systems, Dec 2009, Vancouver, Canada. ⟨hal-01297947⟩

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