| Paper: | MLSP-L1.6 | ||
| Session: | Pattern Recognition and Classification I | ||
| Time: | Thursday, May 20, 11:10 - 11:30 | ||
| Presentation: | Lecture | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | DISSIMILARITY MEASURES IN FEATURE SPACE | ||
| Authors: | Frédéric Desobry; IRCCyN, UMR CNRS 6597 | ||
| Manuel Davy; IRCCyN, UMR CNRS 6597 | |||
| Abstract: | In this paper, we present a study of the statistical behavior of the dissimilarity measure D, proposed in~\cite{Desobry-Icassp-2003} and which results from a machine learning-based quantile estimation approach, namely: single-class support vector machine. This dissimilarity measure possesses the interesting property of being asymptotically equivalent to the Fisher ratio when dealing with radial Gaussian probability density functions. More generally, it can be efficiently applied to non-connected quantiles, and to noisy data sets, as outliers are taken into account by the SVM. A generalisation of D is then proposed, which results in the design of a more general class of dissimilarity measures, also defined in feature space and with the same properties. | ||
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