%0 Journal Article
%T MILDE: multiple instance learning by discriminative embedding
%A Jaume Amores
%J Knowledge and Information Systems
%D 2015
%V 42
%N 2
%I Springer London
%@ 0219-1377
%F Jaume Amores2015
%O ADAS; 601.042; 600.057; 600.076
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2383), last updated on Tue, 15 Dec 2015 12:58:14 +0100
%X While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
%K Multi-instance learning
%K Codebook
%K Bag of words
%U http://refbase.cvc.uab.es/files/Amo2014.pdf
%U http://dx.doi.org/10.1007/s10115-013-0711-1
%P 381-407