A novel incremental one-class support vector machine based on low variance direction

摘要

Low variance direction of the training dataset can carry crucial information when building a performant one-class classifier. Covariance-guided One-Class Support Vector Machine (COSVM) emphasizes the low variance direction of the training dataset which results in higher accuracy. However, in the case of large scale datasets, or sequentially obtained data, it shows a serious performance degradation and requires a large memory and an important training time. Thus, in this paper, we investigate the effectiveness of using the low variance directions in an incremental approach. In fact, incremental learning is more effective when dealing with dynamic or important amount of data. More precisely, we control the possible changes of support vectors after the addition of new data points, while emphasizing the low variance directions of the training data, in order to improve classification performance. An extensive comparison of the incremental COSVM to contemporary batch and incremental one-class classifiers on artificial and real-world datasets demonstrates the advantage and the superiority of our proposed model. (C) 2019 Elsevier Ltd. All rights reserved.