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A second order cone programming approach for semi-supervised learning

机译:用于半监督学习的二阶锥规划方法

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摘要

Semi-supervised learning (SSL) involves the training of a decision rule from both labeled and unlabeled data. In this paper, we propose a novel SSL algorithm based on the multiple clusters per class assumption. The proposed algorithm consists of two stages. In the first stage, we aim to capture the local cluster structure of the training data by using the k-nearest-neighbor (kNN) algorithm to split the data into a number of disjoint subsets. In the second stage, a maximal margin classifier based on the second order cone programming (SOCP) is introduced to learn an inductive decision function from the obtained subsets globally. For linear classification problems, once the kNN algorithm has been performed, the proposed algorithm trains a classifier using only the first and second order moments of the subsets without considering individual data points. Since the number of subsets is usually much smaller than the number of training points, the proposed algorithm is efficient for handling big data sets with a large amount of unlabeled data. Despite its simplicity, the classification performance of the proposed algorithm is guaranteed by the maximal margin classifier. We demonstrate the efficiency and effectiveness of the proposed algorithm on both synthetic and real-world data sets.
机译:半监督学习(SSL)涉及训练来自标记和未标记数据的决策规则。在本文中,我们提出了一种基于每个类假设的多个群集的新颖SSL算法。所提出的算法包括两个阶段。在第一阶段,我们旨在通过使用k最近邻(kNN)算法将训练数据拆分为多个不相交的子集来捕获训练数据的局部聚类结构。在第二阶段,引入基于二阶锥规划(SOCP)的最大余量分类器,以从获得的全局子集中学习归纳决策函数。对于线性分类问题,一旦执行了kNN算法,提出的算法仅使用子集的一阶和二阶矩训练分类器,而无需考虑单个数据点。由于子集的数量通常比训练点的数量小得多,因此所提出的算法对于处理带有大量未标记数据的大数据集非常有效。尽管它很简单,但是最大边缘分类器保证了所提出算法的分类性能。我们在合成数据集和实际数据集上都证明了该算法的效率和有效性。

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