Incremental Classification with Generalized Eigenvalues |
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Authors: | Claudio Cifarelli Mario R Guarracino Onur Seref Salvatore Cuciniello Panos M Pardalos |
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Institution: | (1) University of Rome "La Sapienza", Rome, Italy;(2) National Research Council, Rome, Italy;(3) University of Florida, Gainesville, FL, 32611, USA |
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Abstract: | 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. 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 generalized eigenvalue classifiers, while promising a
comparable performance in classification with respect to the state-of-the-art classification methods. |
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