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Multi-instance data, in which each object (bag) contains a collection ofinstances, are widespread in machine learning, computer vision, bioinformatics,signal processing, and social sciences. We present a maximum entropy (ME)framework for learning from multi-instance data. In this approach each bag isrepresented as a distribution using the principle of ME. We introduce theconcept of confidence-constrained ME (CME) to simultaneously learn thestructure of distribution space and infer each distribution. The sharedstructure underlying each density is used to learn from instances inside eachbag. The proposed CME is free of tuning parameters. We devise a fastoptimization algorithm capable of handling large scale multi-instance data. Inthe experimental section, we evaluate the performance of the proposed approachin terms of exact rank recovery in the space of distributions and compare itwith the regularized ME approach. Moreover, we compare the performance of CMEwith Multi-Instance Learning (MIL) state-of-the-art algorithms and show acomparable performance in terms of accuracy with reduced computationalcomplexity.

Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data