Learning a Layered Graph with a Maximal Number of Distinct Paths Connecting Source and Sink
نویسندگان
چکیده
We discuss the problem of ranking objects sampled from in general r ordered categories. A ranking model for this kind of data can be visualized as a (layered) ranking graph having a single source and sink, and the number of paths connecting source and sink is seen as a ranking-based performance measure. In this article we consider learning such a graph as a structured output in a convex optimization framework. To this end, a new kernelbased algorithm for layered ranking is presented by optimizing the number of distinct paths connecting the source and sink. Although the resulting quadratic program has a huge number of constraints, we show that it can be efficiently optimized with a cutting plane algorithm and graph-based methods. The algorithm scales cubic in the number of training objects.
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