Skip to Main content Skip to Navigation
Conference papers

Using SVDD in SimpleMKL for 3D-Shapes Filtering

Abstract : This paper proposes the adaptation of Support Vector Data Description (SVDD) to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a variant called Slim-MK-SVDD that is able to produce a tighter frontier around the data. For the sake of comparison , the equivalent methods are also developed for One-Class SVM, known to be very similar to SVDD for certain shapes of kernels. Those algorithms are illustrated in the context of 3D-shapes filtering and outliers detection. For the 3D-shapes problem, the objective is to be able to select a sub-category of 3D-shapes, each sub-category being learned with our algorithm in order to create a filter. For outliers detection, we apply the proposed algorithms for unsupervised outliers detection as well as for the supervised case.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Gaelle Loosli <>
Submitted on : Tuesday, September 26, 2017 - 2:45:03 PM
Last modification on : Wednesday, February 24, 2021 - 4:24:01 PM
Long-term archiving on: : Wednesday, December 27, 2017 - 1:19:11 PM


Files produced by the author(s)


  • HAL Id : hal-01593595, version 1



Gaëlle Loosli, Hattoibe Aboubacar. Using SVDD in SimpleMKL for 3D-Shapes Filtering. CAp 2016, Jul 2016, Marseille, France. ⟨hal-01593595⟩



Record views


Files downloads