A Mathematical Optimization Approach to Multisphere Support Vector Data Description
This work addresses outlier detection for complex, non-linear data structures, representing an incremental improvement over existing methods.
The authors tackled outlier detection in multimodal datasets by extending Support Vector Data Description with a novel mathematical optimization framework, resulting in an exact method that outperforms existing heuristic techniques in accuracy and robustness.
We present a novel mathematical optimization framework for outlier detection in multimodal datasets, extending Support Vector Data Description approaches. We provide a primal formulation, in the shape of a Mixed Integer Second Order Cone model, that constructs Euclidean hyperspheres to identify anomalous observations. Building on this, we develop a dual model that enables the application of the kernel trick, thus allowing for the detection of outliers within complex, non-linear data structures. An extensive computational study demonstrates the effectiveness of our exact method, showing clear advantages over existing heuristic techniques in terms of accuracy and robustness.