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Geometrical and computational aspects of Spectral Support Estimation for novelty detection
Year: 2014 Keywords: Support estimation; Kernel methods; Novelty detection
Authors: Alessandro Rudi, Ernesto De Vito, Francesca Odone  
Journal: Pattern Recognition Letters Volume: 35
Pages: 107-116
   
Abstract:
In this paper we discuss the Spectral Support Estimation algorithm (De Vito et al., 2010) by analyzing its 27 geometrical and computational properties. The estimator is non-parametric and the model selection 28 depends on three parameters whose role is clarified by simulations on a two-dimensional space. The performance of the algorithm for novelty detection is tested and compared with its main competitors on a 30 collection of real benchmark datasets of different sizes and types.
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