Geometrical and computational aspects of Spectral Support Estimation for novelty detection

In this paper we discuss the Spectral Support Estimation algorithm [1] 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.