PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploring the hubness-related properties of oceanographic sensor data
Nenad Tomašev and Dunja Mladenić
In: IS 2011, 8-12 Oct 2011, Ljubljana, Slovenia.


In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning methods. We focus on one particular consequence of the curse of dimensionality – hubness. We examine the hubness of oceanographic data and show how it can be used to visualize and detect both prototypical sensors/locations, as well as ambiguous and potentially erroneous ones. We proceed to define an easy classification problem on the data, showing that the recently developed hubness-aware classification methods may help to overcome some of the hubness-related issues in sensor data.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:8725
Deposited By:Jan Rupnik
Deposited On:21 February 2012