Abstract
Computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. A popular approach is the formulation of generalized median as an optimization problem. The concept of generalized median has been studied for numerous problem domains with a broad range of applications. Currently, the research is widely scattered in the literature and no comprehensive survey is available. This brief survey contributes to closing this gap and systematically discusses the relevant issues of generalized median computation. In particular, we present a taxonomy of computation frameworks and methods. We also outline a number of future research directions.
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Acknowledgments
This work was partly supported by the Deutsche Forschungsgemeinschaft (DFG) - CRC 1450 - 431460824 and the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant 778602 ULTRACEPT.
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Jiang, X., Nienkötter, A. (2023). Generalized Median Computation for Consensus Learning: A Brief Survey. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14184. Springer, Cham. https://doi.org/10.1007/978-3-031-44237-7_12
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