Multi-kernel learning has been well explored in the recent past and has
exhibited promising outcomes for multi-class classification and regression
tasks. In this paper, we present a multiple kernel learning approach for the
One-class Classification (OCC) task and employ it for anomaly detection.
Recently, the basic multi-kernel approach has been proposed to solve the OCC
problem, which is simply a convex combination of different kernels with equal
weights. This paper proposes a Localized Multiple Kernel learning approach for
Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is
assigned locally. Proposed LMKAD approach adapts the weight for each kernel
using a gating function. The parameters of the gating function and one-class
classifier are optimized simultaneously through a two-step optimization
process. We present the empirical results of the performance of LMKAD on 25
benchmark datasets from various disciplines. This performance is evaluated
against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four
other existing kernel-based one-class classifiers to showcase the credibility
of our approach. Our algorithm achieves significantly better Gmean scores while
using a lesser number of support vectors compared to MKAD. Friedman test is
also performed to verify the statistical significance of the results claimed in
this paper.

本文提出了一种新的基于局部多核学习和门函数的一类分类算法，用于异常检测，与现有算法相比，该算法在 25 种基准数据集上的性能明显更好，支持向量更少。