Improved Chain-Block Algorithm to RVM on Large Scale Problems

RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M³) time and O(M²) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center as the iteration solution last time,reduce the time complexitiy further more with keeping high accuracy and sparsity simultaneously. Regression experiments with synthetical large benchmark data set demonstrates I-CBA yields state-of-the-art performance.