Approximate polytope ensemble for one-class classification
The methodology uses a convex-hull for modeling one-class classification problems. Random projections are used to approximate the convex-hull in high dimensional spaces. Expansions of the approximate hulls are considered to set the optimal operating point. Exhaustive validation is performed on three different typologies of problems.
one-class classification random projections convex hull high-dimensionality ensemble learning