Lp-Norm Constrained One-Class Classifier Combination
Abstract
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.
Cite
@article{arxiv.2312.15769,
title = {Lp-Norm Constrained One-Class Classifier Combination},
author = {Sepehr Nourmohammadi and Shervin Rahimzadeh Arashloo},
journal= {arXiv preprint arXiv:2312.15769},
year = {2023}
}