  Multi-instance data, in which each object (bag) contains a collection of
instances, 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 is
represented as a distribution using the principle of ME. We introduce the
concept of confidence-constrained ME (CME) to simultaneously learn the
structure of distribution space and infer each distribution. The shared
structure underlying each density is used to learn from instances inside each
bag. The proposed CME is free of tuning parameters. We devise a fast
optimization algorithm capable of handling large scale multi-instance data. In
the experimental section, we evaluate the performance of the proposed approach
in terms of exact rank recovery in the space of distributions and compare it
with the regularized ME approach. Moreover, we compare the performance of CME
with Multi-Instance Learning (MIL) state-of-the-art algorithms and show a
comparable performance in terms of accuracy with reduced computational
complexity.
