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A boosting approach to multiview classification with cooperation

A boosting approach to multiview classification with cooperation

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Nowadays in numerous elds such as bioinformatics or multimedia, data may be described using many dierent sets of features (or views) which carry either global or local information. Many learning tasks make use of these competitive views in order to improve overall predictive power of classiers through fusion-based methods. Usually, these approaches rely on a weighted combination of classiers (or selected descriptions), where classiers are learnt independently the ones from the others. One drawback of these methods is that the classier learnt on one view does not communicate its lack to the other views. In other words, learning algorithms do not cooperate although they are trained on the same objects. This paper deals with a novel approach to integrate multiview information within an iterative learning scheme, where the classier learnt on one view is allowed to somehow communicate its performances to the other views. The proposed algorithm, named Mumbo, is based on boosting. Within the boosting scheme, Mumbo maintains one distribution of examples on each view, and at each round, it learns one weak classier on each view. Within a view, the distribution of examples evolves both with the ability of the dedicated classier to deal with examples of the corresponding features space, and with the ability of classiers in other views to process the same examples within their own description spaces. Hence, the principle is to slightly remove the hard examples from the learning space of one view, while their weights get higher in the other views. This way, we expect that examples are urged to be processed by the most appropriate views, when possible. At the end of the iterative learning process, a nal classier is computed by a weighted combination of selected weak classiers. Such an approach is merely useful when some examples detected as outliers in a view { for instance because of noise { are quite probabilisticaly regular hence informative within some other view. This paper provides the Mumbo algorithm in a multiclass and multiview setting, based on recent advances in theoretical boosting. The boosting properties of Mumbo are proven, as well as a some results on its generalization capabilities. Several experimental results are reported which point out that complementary views may actually cooperate under some assumptions.


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