$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space
This work addresses one-class classification tasks in machine learning, but it appears incremental as it builds on existing Fisher null-space methods with a norm constraint.
The paper tackled the multiple kernel learning problem for one-class classification by proposing an algorithm based on the Fisher null-space principle with an ℓp-norm constraint on kernel weights, and extensive experiments on various datasets confirmed its merits against baseline and other algorithms.
The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space one-class classification principle, we present a multiple kernel learning algorithm where a general $\ell_p$-norm constraint ($p\geq1$) on kernel weights is considered. We cast the proposed one-class MKL task as a min-max saddle point Lagrangian optimisation problem and propose an efficient method to solve it. An extension of the proposed one-class MKL approach is also considered where several related one-class MKL tasks are learned jointly by constraining them to share common kernel weights. An extensive assessment of the proposed method on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.