Abstract
Prototype-based models such as learning vector quantization (LVQ) enjoy a wide popularity because they combine excellent classification and generalization ability with an intuitive learning paradigm: models are represented by few characteristic prototypes, the latter often being located at class typical positions in the data space. In this article we investigate inhowfar these expectations are actually met by modern LVQ schemes such as robust soft LVQ and generalized LVQ. We show that the mathematical models do not explicitly optimize the objective to find representative prototypes. We demonstrate this fact in a few benchmarks. Further, we investigate the behavior of the models if this objective is explicitly formalized in the mathematical costs. This way, a smooth transition of the two partially contradictory objectives, discriminative power versus model representativity, can be obtained.
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Hammer, B., Nebel, D., Riedel, M., Villmann, T. (2014). Generative versus Discriminative Prototype Based Classification. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_12
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DOI: https://doi.org/10.1007/978-3-319-07695-9_12
Publisher Name: Springer, Cham
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