Fast Laplacian twin support vector machine with active learning for pattern classification

In this paper, we propose a semi-supervised classifier termed as Fast Laplacian Twin Support Vector Machine (FLap - TWSVM) with an objective to reduce the requirement of labeled data and simultaneously lessen the training time complexity of a traditional Laplacian Twin Support Vector Machine semisupervised classifier. FLap - TWSVM is faster than existing Laplacian twin support vector machine as it solves a smaller size Quadratic Programming Problem (QPP) along with an Unconstrained Minimization Problem (UMP) to obtain decision hyperplanes which can also handle heteroscedastic noise present in the training data. Traditional semi-supervised classifiers generally have no explicit control over the choice of labeled data available for training, hence to overcome this limitation, we propose a pool-based active learning framework which identifies most informative examples to train the learning model. Moreover, the aforementioned framework has been extended to deal with multi-category classification scenarios. Several experiments have been performed on machine learning benchmark datasets which proves the utility of the proposed classifier over traditional Laplacian Twin Support Vector Machine (Lap - TWSVM) and active learning based Support Vector Machine (SVMAL). The efficacy of the proposed framework has also been tested on human activity recognition problem and content based image retrieval system. (C) 2018 Elsevier B.V. All rights reserved.