In the past decade the development of automatic intrinsic dimensionality estimators has gained considerable attention due to its relevance in several application fields. However, most of the proposed solutions prove to be not robust on noisy datasets, and provide unreliable results when the intrinsic dimensionality of the input dataset is high and the manifold where the points are assumed to lie is nonlinearly embedded in a higher dimensional space. In this paper we propose a novel intrinsic dimensionality estimator (DANCo) and its faster variant (FastDANCo), which exploit the information conveyed both by the normalized nearest neighbor distances and by the angles computed on couples of neighboring points. The effectiveness and robustness of the proposed algorithms are assessed by experiments on synthetic and real datasets, by the comparative evaluation with state-of-the-art methodologies, and by significance tests.
DANCo : an intrinsic dimensionality estimator exploiting angle and norm concentration / C. Ceruti, S. Bassis, A. Rozza, G. Lombardi, E. Casiraghi, P. Campadelli. - In: PATTERN RECOGNITION. - ISSN 0031-3203. - 47:8(2014 Aug), pp. 2569-2581.
|Titolo:||DANCo : an intrinsic dimensionality estimator exploiting angle and norm concentration|
|Parole Chiave:||Intrinsic dimensionality estimation; Kullback-Leibler divergence; Manifold learning; Nearest neighbor distance distribution; Von Mises distribution; Software; Artificial Intelligence; 1707; Signal Processing|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
Settore MAT/06 - Probabilita' e Statistica Matematica
|Data di pubblicazione:||ago-2014|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.patcog.2014.02.013|
|Appare nelle tipologie:||01 - Articolo su periodico|