A bag-to-class divergence approach to multiple-instance learning

Kajsa Møllersen; Jon Yngve Hardeberg; Fred Godtliebsen

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arXiv · 1803.02782

A bag-to-class divergence approach to multiple-instance learning

Kajsa Møllersen·Jon Yngve Hardeberg·Fred Godtliebsen

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Abstract

In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is the distribution, not the realisations. In MI classification, each bag in the training set has a class label, but the instances are unlabelled. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback-Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasising the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets.

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BibTeX RIS

Kajsa Møllersen, Jon Yngve Hardeberg, Fred Godtliebsen. 2018-03-07. A bag-to-class divergence approach to multiple-instance learning. https://arxiv.org/abs/1803.02782

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