  Multi-instance learning (MIL) has a wide range of applications due to its
distinctive characteristics. Although many state-of-the-art algorithms have
achieved decent performances, a plurality of existing methods solve the problem
only in instance level rather than excavating relations among bags. In this
paper, we propose an efficient algorithm to describe each bag by a
corresponding feature vector via comparing it with other bags. In other words,
the crucial information of a bag is extracted from the similarity between that
bag and other reference bags. In addition, we apply extensions of Hausdorff
distance to representing the similarity, to a certain extent, overcoming the
key challenge of MIL problem, the ambiguity of instances' labels in positive
bags. Experimental results on benchmarks and text categorization tasks show
that the proposed method outperforms the previous state-of-the-art by a large
margin.
