A Hash Based Method for Large Scale Nonparallel Support Vector Machines Prediction

• Published in: Procedia computer science Vol. 108; pp. 1281 - 1291
• Main Authors: Ju, Xuchan, Wang, Tianhe
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2017
• Subjects: Hash; Large scale data sets; Nonparallel support vector machine; Prediction; Nonparallel support vector machine; Hash; Large scale data sets; Prediction
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2017.05.133

Recent years have witnessed more and more success of hash methods for building efficient classifiers, but less for prediction in machine learning. In this paper, we propose a hash based method for large scale nonparallel support vector machine prediction(HNPSVM). Our key idea of this method is that we use an approximal decision function instead of exact decision function by computing the Hamming distance between hashing the normal to the hyperplane of the classifier and the features. This method benefits nonparallel support vector(NPSVM) prediction in three aspects. First, it enhances the prediction accuracy using an flexible and general method. Second, the proposed HNPSVM reduce storage cost owing to the compact binary hash representation. Last, HNPSVM can speed up the computation of classification function. Moreover, we prove that the classification results of a hash based NPSVM classifier converge to the results of the exact NPSVM classifier as the number of binary hash functions tends to infinity. Several experiments on large scale data sets show the efficient of our method.