Incremental Classification with Generalized Eigenvalues

Supervised learning techniques are widely accepted methods to analyze data for scientific and real world problems. Most of these problems require fast and continuous acquisition of data, which are to be used in training the learning system. Therefore, maintaining such systems updated may become cumbersome. Various techniques have been devised in the field of machine learning to solve this problem. In this study, we propose an algorithm to reduce the training data to a substantially small subset of the original training data to train a generalized eigenvalue classifier (GEC). The proposed method provides a constructive way to understand the influence of new training data on an existing classification function. We show through numerical experiments that this technique prevents the overfitting problem of the earlier GEC methods, while promising a comparable performance in classification with respect to the state-of-the-art classification methods.