标题： 单类半监督学习：通过均值检测线性可分类别 显示英文标题

标题： One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean

摘要： 本文提出了一种新颖的半监督单类分类算法，该算法假设类别可以从其他元素中线性分离。 我们从理论上证明了类别是线性可分的当且仅当在具有相同均值的集合中它是概率最大的。 此外，我们提出了一个利用线性规划识别此类线性可分类别的算法。 我们描述了三个应用案例，包括线性可分性的假设、高斯分布以及核函数变换空间中的线性可分性情况。 最后，我们在USPS数据集上展示了所提出的算法的工作原理，并分析了算法性能与初始标记样本大小之间的关系。 显示英文摘要

摘要： In this paper, we presented a novel semi-supervised one-class classification algorithm which assumes that class is linearly separable from other elements. We proved theoretically that class is linearly separable if and only if it is maximal by probability within the sets with the same mean. Furthermore, we presented an algorithm for identifying such linearly separable class utilizing linear programming. We described three application cases including an assumption of linear separability, Gaussian distribution, and the case of linear separability in transformed space of kernel functions. Finally, we demonstrated the work of the proposed algorithm on the USPS dataset and analyzed the relationship of the performance of the algorithm and the size of the initially labeled sample.