Uncertainty in Artificial Intelligence
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The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking
Rishabh Iyer, Jeff Bilmes
Abstract:
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an "ordering", thus providing a new view of rank aggregation and order based clustering with interesting connections to web ranking. We show how the LB divergences have a number of properties akin to many permutation based metrics, and in fact have as special cases forms very similar to the Kendall-tau metric. We also show how the LB divergences subsume a number of commonly used ranking measures in information retrieval, like NDCG and AUC. Unlike the traditional permutation based metrics, however, the LB divergence naturally captures a notion of "confidence" in the orderings, thus providing a new representation to applications involving aggregating scores as opposed to just orderings. We show how a number of recently used web ranking models are forms of Lovasz-Bregman rank aggregation and also observe that a natural form of Mallow's model using the LB divergence has been used as conditional ranking models for the "Learning to Rank" problem.
Keywords:
Pages: 321-330
PS Link:
PDF Link: /papers/13/p321-iyer.pdf
BibTex:
@INPROCEEDINGS{Iyer13,
AUTHOR = "Rishabh Iyer and Jeff Bilmes",
TITLE = "The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "321--330"
}


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