Large-Scale Graph-based Transductive Inference

author: Jeff A. Bilmes, Department of Electrical Engineering, University of Washington
published: Jan. 19, 2010,   recorded: December 2009,   views: 3671

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Description

We consider the issue of scalability of graph-based semi-supervised learning (SSL) algorithms. In this context, we propose a fast graph node ordering algorithm that improves parallel spatial locality by being cache cognizant. This approach allows for a linear speedup on a shared-memory parallel machine to be achievable, and thus means that graph-based SSL can scale to very large data sets. We use the above algorithm an a multi-threaded implementation to solve a SSL problem on a 120 million node graph in a reasonable amount of time.

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