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1 2006

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Accelerating the relevance vector machine via data partitioning

Ben-Shimon D., Shmilovici A.

Foundations of Computing and Decision Sciences

2006

Vol. 31, No. 1

27-41

The Relevance Vector Machine (RVM) is a method for training sparse generalized linear models, and its accuracy is comparably to other machine learning techniques. For a dataset of size N the runtime complexity of the RVM is O(NJ) and its space complexity is O(N2) which makes it too expensive for moderately sized problems. We suggest three different algorithms which reduce the runtime complexity to O(N") via partitioning the dataset into small chunks of size P. A heuristic is presented for selecting the chunk size. Extensive experiments with benchmark datasets indicate that the partition algorithms can significantly reduce the complexity of the RVM while retaining the attractive attributes of the original solution.