Binary classification refers to supervised techniques that split a set of points in two classes, with respect to a training set of points whose membership is known for each class. Binary classification plays a central role in the solution of many scientific, financial, engineering, medical and biological problems. Many methods with good classification accuracy are currently available. This work shows how a binary classification problem can be expressed in terms of a generalized eigenvalue problem. A new regularization technique is proposed, which gives results that are comparable to other techniques in use, in terms of classification accuracy. The advantage of this method relies in its lower computational complexity with respect to the existing techniques based on generalized eigenvalue problems. Finally, the method is compared with other methods using benchmark data sets.
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Generalized Eigenvalue problem
Document Type: Research Article
High Performance Computing and Networking Institute, National Research Council, Italy
Department of Statistic, Probability and Applied Statistics, University of Rome 'La Sapienza', Italy
Center for Applied Optimization, University of Florida, Gainesville, FL, USA
Publication date: 01 February 2007