A novel incremental Kernel Nonparametric SVM model (iKN-SVM) for data classification: An application to face detection

In this paper, we propose a novel incremental classifier to overcome problems associated with batch techniques, along with issues related to data spread that Kernel Support Vector Machines (KSVM) may encounter. Basically, we present a Kernel SVM-based model that learns incrementally, as new data is available over time, in order to handle dynamic and large data effectively and reduce the computational time. The proposed model deals with the data spread issues by introducing near-global variations, from the scatter matrices of the Kernel Nonparametric Discriminant Analysis (KNDA), into the optimization problem of incremental KSVM, while considering local characteristics of the data provided by KSVM. Besides, our model has a quadratic convex optimization problem with one global solution. Furthermore, an extensive comparison of the model with other state-of-the-art incremental and batch algorithms on various datasets, has been carried out, in order to show its advantages and effectiveness for classification tasks. Moreover, an evaluation of the proposed method on face detection is provided.