论文标题
椭圆形子空间支持向量数据描述
Ellipsoidal Subspace Support Vector Data Description
论文作者
论文摘要
在本文中，我们提出了一种新颖的方法，将数据转换为针对一级分类优化的低维空间。所提出的方法迭代将数据转换为针对目标类数据的椭圆形封装的新的子空间。我们为提出的方法提供线性和非线性公式。该方法考虑了子空间中数据的协方差；因此，与Sypersphere的子空间支持矢量数据描述相比，它产生了更具概括的解决方案。我们提出了不同的正则化术语，以表达投影空间中的班级差异。我们将结果与经典且最近提出的一级分类方法进行比较，并在大多数情况下获得更好的结果。还注意到该方法的收敛速度比最近提出的子空间支持向量数据描述快得多。
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to Subspace Support Vector Data Description for a hypersphere. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve better results in the majority of cases. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
