Make it cheap: learning with O(nd) complexity

Abstract

Learning methods with linear computational complexity O(nd) in number of samples and their dimension often give results that are better or at least not worse that more sophisticated and slower algorithms. This is demonstrated for many benchmark datasets downloaded from the UCI Machine Learning Repository. Results provided in this paper should be used as a reference for estimating usefulness of new learning algorithms. Methods with higher than linear complexity should provide significantly better results than those presented in this paper to justify their use.