论文标题
美元
$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space
论文作者
论文摘要
本文解决了单级分类的多个内核学习（MKL）问题（OCC）。为此，基于Fisher Null空间一级分类原理，我们提出了一种多个内核学习算法，其中考虑了一般的$ \ ell_p $ -norm约束（$ p \ geq1 $），却考虑了内核重量。我们将提出的单级MKL任务作为Min-Max Saddle Point Lagrangian优化问题，并提出了一种解决该问题的有效方法。还考虑了所提出的单级MKL方法的扩展，即通过约束它们共享共同的内核权重，共同学习了几个相关的单级MKL任务。 对来自不同应用领域的一系列数据集的提议方法的广泛评估证实了其针对基线和其他几种算法的优点。
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.
