Geometrical and computational aspects of Spectral Support Estimation for novelty detection

• 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.