Generalized eigenvalue proximal support vector machines for outlier description
Generalized eigenvalue proximal support vector machines for outlier description
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DOI：
10.1109/ijcnn.2015.7280343
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
F. Dufrenois;J. Noyer
中科院分区：
文献类型：
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作者：
F. Dufrenois;J. Noyer
在本文中，我们提出将多曲面近邻支持向量机扩展到孤立点检测问题。我们没有考虑两个不平行的邻近平面来提取类，而是只寻找一个离目标或优势种群最近的平面，并尽可能远离离群点。根据这一结果，我们表明，对标准的简单修改引入了一种有效的对比度量，以将目标或主要数据总体与离群值分开。引入核技巧，将该算法推广到非线性数据集。在合成数据集和真实数据集上，将该算法与最近的新颖性检测器进行了比较。
In this paper, we propose to extend the multisurface proximal support vector machines to the problem of outlier detection. Instead of considering two non parallel proximal planes for extracting classes, we only seek a plane which is proximal to the target or dominant population and as far as possible from outliers. From this result, we show that a simple modification of the criterion introduces an effective contrast measure to isolate a target or dominant data population from outliers. Introducing the kernel trick, we extend the proposed algorithm to nonlinear data sets. The proposed algorithm is compared with recent novelty detectors on synthetic and real data sets.