Exploring the hubness-related properties of oceanographic sensor data
published: Nov. 4, 2011, recorded: October 2011, views: 70
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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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