Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Kernel PCA for novelty detection
Pattern Recognition
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Spectral algorithms for supervised learning
Neural Computation
ACM Computing Surveys (CSUR)
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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.