"Johnson–Lindenstrauss lemma"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q6268577 Q6268577]

2020년 12월 26일 (토) 05:27 판

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말뭉치

  1. The Johnson-Lindenstrauss Lemma (JL lemma) tells us that we need dimension , and that the mapping is a (random) linear mapping.[1]
  2. A variant of the Johnson–Lindenstrauss lemma for circulant matrices.[2]
  3. A simple short proof of the Johnson-Lindenstrauss lemma (concerning nearly isometric embeddings of finite point sets in lower-dimensional spaces) is given.[3]
  4. Earlier versions of the Johnson-Lindenstrauss lemma used a slightly different function .[4]
  5. It turns out that the Johnson-Lindenstrauss lemma is almost optimal.[4]
  6. Then, if we map the ‘s to points in while preserving distances up to a factor , then the dimension must be at least , which is nearly what the Johnson-Lindenstrauss lemma would give.[4]
  7. The Johnson-Lindenstrauss lemma very strongly depends on properties of the Euclidean norm.[4]

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