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MILDE: multiple instance learning by discriminative embedding

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Abstract

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.

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Notes

  1. Furthermore, in practice, the repetition of elements occurs very rarely. This is due to the fact that the elements \(\vec {x}_j\) are almost always defined as vectors of real components, i.e., \(\vec {x}_j \in \mathbb R^d\) for \(j = 1,\ldots ,N.\)

  2. Given a multi-class problem, it can be reduced to several binary classification problems by means of common strategies such as one-against-all.

  3. In order to simplify the discussion, we consider only clusters of positive instances in this explanation. However, the same idea can be used in clusters of negative instances. In this case, a cluster is contaminated if not only contains negative instances but it also contains many positive ones.

  4. Regarding the negative instances, they were generated by using a constant number of Gaussian components, \(N_n=32\). We used a large number of components for the negative class in order to obtain realistic data. In this sense, note that the negative class is usually defined as the negation of the positive class, i.e., it groups all the classes of objects that are not positive, and thus forms an heterogeneous class.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This work was supported by the fellowship RYC-2008-03789 and the Spanish project TRA2011-29454-C03-01.

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  1. Computer Vision Center, Universtitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

    Jaume Amores

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  1. Jaume Amores
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Correspondence to Jaume Amores.

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Amores, J. MILDE: multiple instance learning by discriminative embedding. Knowl Inf Syst 42, 381–407 (2015). https://doi.org/10.1007/s10115-013-0711-1

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