{
  "id": "2011.03904",
  "version": "v1",
  "published": "2020-11-08T05:27:50.000Z",
  "updated": "2020-11-08T05:27:50.000Z",
  "title": "Locally Adaptive Nearest Neighbors",
  "authors": [
    "Jan Philip Göpfert",
    "Heiko Wersing",
    "Barbara Hammer"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "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. 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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-08T05:27:50.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "locally adaptive nearest neighbors",
      "real-world benchmark data sets",
      "synthetic data sets",
      "demonstrate important aspects",
      "nearest neighbors algorithms"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}