Accelerating the relevance vector machine via data partitioning
David Ben-Shimon, Armin Shmilovici · Foundations of Computing and Decision Sciences · 2006
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(N) and its space complexity is O(N) which makes it too expensive for moderately sized problems. We suggest three different algorithms which partition the dataset into manageable chunks. Our experiments on benchmark datasets indicate that the partition algorithms can significantly reduce the complexity of the RVM while retaining the attractive attributes of the original solution.