Article ID Journal Published Year Pages File Type
533916 Pattern Recognition Letters 2014 10 Pages PDF
Abstract

•We discuss separating kernels, providing a geometrical interpretation of the property.•We review the SSE algorithm, emphasizing its geometrical and computational aspects.•We discuss the SSE dependence on its hyper-parameters.•We show the effectiveness of the SSE for novelty detection on real benchmark datasets.

In this paper we discuss the Spectral Support Estimation algorithm (De Vito et al., 2010) by analyzing its geometrical and computational properties. The estimator is non-parametric and the model selection 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 collection of real benchmark datasets of different sizes and types.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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