Anomaly Classification with the Anti-Profile Support Vector Machine
This work addresses anomaly classification for applications like cancer genomics, but it appears incremental as it extends SVM with a new kernel method.
The paper tackles the anomaly classification problem, where data samples from multiple anomalous classes must be distinguished based on deviation from a normal class, and introduces the anti-profile SVM (apSVM) as a novel algorithm that improves accuracy and stability over standard SVM in simulations and cancer genomics datasets.
We introduce the anti-profile Support Vector Machine (apSVM) as a novel algorithm to address the anomaly classification problem, an extension of anomaly detection where the goal is to distinguish data samples from a number of anomalous and heterogeneous classes based on their pattern of deviation from a normal stable class. We show that under heterogeneity assumptions defined here that the apSVM can be solved as the dual of a standard SVM with an indirect kernel that measures similarity of anomalous samples through similarity to the stable normal class. We characterize this indirect kernel as the inner product in a Reproducing Kernel Hilbert Space between representers that are projected to the subspace spanned by the representers of the normal samples. We show by simulation and application to cancer genomics datasets that the anti-profile SVM produces classifiers that are more accurate and stable than the standard SVM in the anomaly classification setting.