Lp-Norm Constrained One-Class Classifier Combination
Sepehr Nourmohammadi Shervin Rahimzadeh Arashloo
Classifier fusion is established as an effective methodology for boosting
performance in different settings and one-class classification is no exception.
In this study, we consider the one-class classifier fusion problem by modelling
the sparsity/uniformity of the ensemble. To this end, we formulate a convex
objective function to learn the weights in a linear ensemble model and impose a
variable Lp-norm constraint on the weight vector. The vector-norm constraint
enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble
in the space of base learners and acts as a (soft) classifier selection
mechanism by shaping the relative magnitudes of fusion weights. Drawing on the
Frank-Wolfe algorithm, we then present an effective approach to solve the
formulated convex constrained optimisation problem efficiently. We evaluate the
proposed one-class classifier combination approach on multiple data sets from
diverse application domains and illustrate its merits in comparison to the
existing approaches.