Lightly trained support vector data description for novelty detection

Anomaly (or outlier) detection is well researched objective in data mining due to its importance and inherent challenges. An outlier could be the key discovery to be made from large datasets and the insights gathered from them could be of significance in a wide variety of domains like information security, business intelligence, clinical decision support, financial monitoring etc. Recently, Support Vector Data Description (SVDD) driven approaches are shown as having good predictive accuracy. This paper proposes a novel low-complexity anomaly detection algorithm based on Support Vector Data Description (SVDD). The proposed algorithm reduces the complexity by avoiding the calculation of Lagrange multipliers of an objective function, instead locates an approximate pre-image of the SVDD sphere's center, within the input space itself. The crux of the training algorithm is a gradient descent of the primal objective function using Simultaneous Perturbation Stochastic Approximation (SPSA). Experiments using datasets obtained from UCI machine learning repository have demonstrated that the accuracies of the proposed approach are comparable while the training time is much lesser than Classical SVDD.