Arashloo, Shervin Rahimzadeh2024-03-192024-03-192023-03-141057-7149https://hdl.handle.net/11693/114962We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an ℓp -norm regularisation ( p≥1 ) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.EnglishCC BYhttps://creativecommons.org/licenses/by/4.0/One-class classificationMultiple kernel learningOne-class Fisher null transformationℓp-norm regularisationArticle10.1109/TIP.2023.3255102