Bag dissimilarities for multiple instance learning

Authors:
David M. J. Tax;Marco Loog;Robert P. W. Duin;Veronika Cheplygina;Wan-Jui Lee
Affiliations:
Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands;Pattern Recognition Laboratory, Delft University of Technology, Delft, The Netherlands
Venue:
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Year:
2011

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Abstract

When objects cannot be represented well by single feature vectors, a collection of feature vectors can be used. This is what is done in Multiple Instance learning, where it is called a bag of instances. By using a bag of instances, an object gains more internal structure than when a single feature vector is used. This improves the expressiveness of the representation, but also adds complexity to the classification of the object. This paper shows that for the situation that not a single instance determines the class label of a bag, simple bag dissimilarity measures can significantly outperform standard multiple instance classifiers. In particular a measure that computes just the average minimum distance between instances, or a measure that uses the Earth Mover's distance, perform very well.