Prototypes within Minimum Enclosing Balls

Author: C. Bauckhage, R. Sifa, T. Dong
Journal: ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions
Year: 2019

Citation information

C. Bauckhage, R. Sifa, T. Dong,
ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions,
2019,
365-376,
Springer, Cham,
https://doi.org/10.1007/978-3-030-30493-5_36

We revisit the kernel minimum enclosing ball problem and show that it can be solved using simple recurrent neural networks. Once solved, the interior of a ball can be characterized in terms of a function of a set of support vectors and local minima of this function can be thought of as prototypes of the data at hand. For Gaussian kernels, these minima can be naturally found via a mean shift procedure and thus via another recurrent neurocomputing process. Practical results demonstrate that prototypes found this way are descriptive, meaningful, and interpretable.