Exploring the hubness-related properties of oceanographic sensor data

author: Nenad Tomašev, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Nov. 4, 2011,   recorded: October 2011,   views: 2753
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Description

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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Download slides icon Download slides: sikdd2011_tomasev_oceanographic_01.pdf (571.2 KB)


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