Abstract
We propose a new framework for the pairwise data clustering problem which is based on a “standard” quadratic program formulation, i.e., a continuous quadratic optimization problem with simplex (or probability) constraints. We then introduce a wide family of dynamic equations from evolutionary game theory, known as payoff-monotonic dynamics, and study their dynamical properties. These properties make any member of this family a potential heuristic for solving standard quadratic programs and, in particular, our data clustering problem. We demonstrate the potential of our framework for the problem of the unsupervised learning of shape categories from an image database. Experiments with two different similarity matrices (and databases) reported in the computer vision literature have been conducted, and the results confirm the effectiveness of our approach.
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Pavan, M., Pelillo, M. (2003). Pairwise Data Clustering Using Monotone Game Dynamics. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_17
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DOI: https://doi.org/10.1007/978-3-540-39853-0_17
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