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• W3203771827 abstract "When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance but are naturally interpretable. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets." @default.
• W3203771827 created "2021-10-11" @default.
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• W3203771827 date "2020-11-08" @default.
• W3203771827 modified "2023-09-27" @default.
• W3203771827 title "Interpretable Locally Adaptive Nearest Neighbors" @default.
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