In the past two decades the estimation of the intrinsic dimensionality of a dataset has gained considerable importance, since it is a relevant information for several real life applications. Unfortunately, although a great deal of research effort has been devoted to the development of effective intrinsic dimensionality estimators, the problem is still open. For this reason, in this paper we propose a novel robust intrinsic dimensionality estimator that exploits the information conveyed by the normalized nearest neighbor distances, through a technique based on rank-order statistics that limits common underestimation issues related to the edge effect. Experiments performed on both synthetic and real datasets highlight the robustness and the effectiveness of the proposed algorithm when compared to state-of-the-art methodologies.
|Titolo:||A novel intrinsic dimensionality estimator based on rank-order statistics|
|Parole Chiave:||intrinsic dimensionality estimation; manifold learning; rank-order statistics|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2015|
|Digital Object Identifier (DOI):||10.1007/978-3-662-48577-4_7|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|