Large-Scale Linear NPSVM via One Permutation Hashing

Nonparallel support vector machine (NPSVM) is a novel nonparallel classifier for binary classification with large amounts of theoretical and practical advantages. How to train NPSVM efficiently on data with huge dimensions has not been studied. Recently, a variety of minwise hashing algorithms such as b-bit minwise hashing, connected bit minwise hashing and f-fractional bit minwise hashing are effectively applied to obtain a compact representation of the original data. However, they still have serious defects that the generation of k random permutations is time-consuming and the processing of the original dataset damages data structure. Fortunately, a simple and effective solution called one permutation hashing appears to avoid the disadvantages of the expensive preprocessing cost and the destruction of the original dataset. In this paper, we combine one permutation hashing scheme with linear NPSVM to speed up the training and testing phases for classification on large-scale and high dimensional datasets. Both theoretical analyses and experiments demonstrate that our algorithm achieves massive advantages in accuracy, efficiency and energy-consumption.