图- 嵌入式子空间支持矢量数据说明 (Graph-Embedded Subspace Support Vector Data Description)

from arxiv, 25 pages manuscript (3 tables, 4 figures), 63 pages supplementary material (43 tables, 34 figures). The manuscript and supplementary material are combined as a single .pdf (88 pages) file

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.

翻译：在本文中,我们提议了一个用于单级分类的新颖的子空间学习框架。拟议框架以图嵌入的形式提出问题。它包括以前提议的子空间单级技术,作为其特例,并进一步深入了解这些技术的实际优化之处。框架通过图保存标准将其他有意义的优化目标纳入其中,并揭示光谱解决方案和光谱回归解决方案,作为以前使用的梯度基技术的替代物。我们把子空间学习框架与在子空间应用的支持矢量数据说明相迭地结合,用于绘制图嵌入的子空间支持矢量数据说明。我们实验分析了新提出的不同变体的性能。我们展示了相对于基线和最近提议的单级分类子空间学习方法的改进性能。