| Paper: | SPTM-P10.8 | ||
| Session: | Estimation | ||
| Time: | Thursday, May 18, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | Estimation of minimum measure sets in reproducing kernel Hilbert spaces and applications | ||
| Authors: | Manuel Davy, CNRS, France; Frederic Desobry, University of Cambridge, United Kingdom; Stephane Canu, INSA Rouen, France | ||
| Abstract: | Minimum measure sets (MMSs) summarize the information of a (single-class) dataset. In many situations, they can be preferred to estimated probability density functions (pdfs): they are strongly related to pdf level sets while being much easier to estimate in large dimensions. The main contribution of this paper is a theoretical connection between MMSs and one class Support Vector Machines. This justifies the use of one-class SVMs in the following applications: novelty detection (we give explicit convergence rate) and change detection. | ||
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