Exploring The Hubness-Related Properties of Oceanographic Sensor Data

Year: 
2011
Publication Date: 
Monday, 10 October, 2011
Published in: 
Conference on Data Mining and Data Warehouses
Authors: 
Nenad Tomašev, Dunja Mladenić

This publications was submitted to the Conference on Data Mining and Data Warehouses (SiKDD 2011), 10 October 2011, Ljubljana, Slovenia.

Abstract: 

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.

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