论文标题
高维数据的一致的半监督图正则化
Consistent Semi-Supervised Graph Regularization for High Dimensional Data
论文作者
论文摘要
半监督的拉普拉斯正则化是一种基于图形和未标记数据的基于图形的标准方法，最近被证明具有微不足道的高维度学习效率（Mai和Couillet 2018）（MAI和Couillet 2018），从而使其不受欢迎的相对方案，相互贴心的效果不足。经过有关此不一致问题的起源的详细讨论，提出了一种涉及居中操作的新型正则化方法作为解决方案，并得到理论分析和经验结果的支持。
Semi-supervised Laplacian regularization, a standard graph-based approach for learning from both labelled and unlabelled data, was recently demonstrated to have an insignificant high dimensional learning efficiency with respect to unlabelled data (Mai and Couillet 2018), causing it to be outperformed by its unsupervised counterpart, spectral clustering, given sufficient unlabelled data. Following a detailed discussion on the origin of this inconsistency problem, a novel regularization approach involving centering operation is proposed as solution, supported by both theoretical analysis and empirical results.
