Predicting Switching Graph Labelings with Cluster Specialists
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We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We provide three mistake-bounded algorithms based on three paradigmatic methods for online algorithm design. The algorithm with the strongest guarantee is a quasi-Bayesian classifier which requires O(t n) time to predict at trial t on an n-vertex graph. The fastest algorithm (with the weakest guarantee) is based on a specialist [10] approach and surprisingly only requires O( n) time on any trial t. We also give an algorithm based on a kernelized Perceptron with an intermediate per-trial time complexity of O(n) and a mistake bound which is not strictly comparable. Finally, we provide experiments on simulated data comparing these methods.
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