Mutual Information Estimation in Higher Dimensions: A Speed-Up of a k-Nearest Neighbor Based Estimator.

Veröffentlichungen: Beitrag in BuchBeitrag in KonferenzbandPeer Reviewed

Abstract

We focus on the recently introduced nearest neighbor based entropy estimator from Kraskov, Stögbauer and Grassberger (KSG), the nearest neighbor search of which is performed by the so called box assisted algorithm. We compare the performance of KSG with respect to three spatial indexing methods: box-assisted, k-D trie and projection method, on a problem of mutual information estimation of a variety of pdfs and dimensionalities. We conclude that the k-D trie method is significantly faster then box-assisted search in fixed-mass and fixed-radius neighborhood searches in higher dimensions. The projection method is much slower than both alternatives and not recommended for practical use.
OriginalspracheEnglisch
TitelProceedings of International Conference on Adaptive and Natural Computing Algorithms (ICANNGA)
UntertitelAdaptive and Natural Computing Algorithms
Herausgeber (Verlag)Springer
Seiten790-797
DOIs
PublikationsstatusVeröffentlicht - 2007

ÖFOS 2012

  • 102033 Data Mining
  • 101016 Optimierung
  • 102019 Machine Learning

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