Chain-Block Algorithm to RVM on Large scale problems
RVM enables sparse classi?cation and regression functions to be obtained by linearly-weighting a small number of ?xed 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 CBA. it decomposed large datasets to subdata blocks by sampled homogeneously and getted solution by chain iteration taking TOA as basis algorithm,. Regression experiments with synthetical large sbenchmark data set demonstrates CBA yielded state-of-the-art performance: its time complexity is linear in M and space complexity is independent of M,keeping high accuracy and sparsity at the same time. Document shows that CBA is also much better than TFA on time complexity and sparsity.
RVM CBA machine learning regression
GangLi Shu-BaoXing Hui-feng Xue GangLi
College of Automation Northwestern Polytechnical University Xi’an Shaanxi 710072，China School of Economics & Management, Xi’an Technological University Xi’an Shaanxi, 710032，China
国际会议
北京
英文
1106-1109
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）