  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.

Machine Learning;Machine Learning