A new estimator of intrinsic dimension based on the multipoint Morisita index

PATTERN RECOGNITION

卷:48

Article

Golay, Jean¹  Kanevski, Mikhail¹ 

[1] Univ Lausanne, Fac Geosci & Environm, Inst Earth Surface Dynam, CH-1015 Lausanne, Switzerland

关键词: Intrinsic dimension;    Multipoint Morisita index;    Fractal dimension;    Multifractality;    Dimensionality reduction;   

DOI  :  10.1016/j.patcog.2015.06.010

来源: Elsevier

【 摘 要 】

The size of datasets has been increasing rapidly both in terms of number of variables and number of events. As a result, the empty space phenomenon and the curse of dimensionality complicate the extraction of useful information. But, in general, data lie on non-linear manifolds of much lower dimension than that of the spaces in which they are embedded. In many pattern recognition tasks, learning these manifolds is a key issue and it requires the knowledge of their true intrinsic dimension. This paper introduces a new estimator of intrinsic dimension based on the multipoint Morisita index. It is applied to both synthetic and real datasets of varying complexities and comparisons with other existing estimators are carried out. The proposed estimator turns out to be fairly robust to sample size and noise, unaffected by edge effects, able to handle large datasets and computationally efficient. (C) 2015 Elsevier Ltd. All rights reserved.

【 授权许可】

Free