Extension of maximal marginal diversity based feature selection applied to underwater acoustic data

This paper addresses the feature selection problem encountered in underwater acoustic data mining. Feature selection is a preamble of any data mining algorithm, allowing a priori dimension reduction and better interpretation of data. Here, we propose a new feature selection technique, based on the maximum marginal diversity principle. Our approach is applied on various real dataset, including underwater acoustic data.