A semi-supervised random vector functional-link network based on the transductive framework
A semi-supervised random vector functional-link network based on the transductive framework
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DOI：
10.1016/j.ins.2015.07.060
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Simone Scardapane;D. Comminiello;M. Scarpiniti;A. Uncini
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文献类型：
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作者：
Simone Scardapane;D. Comminiello;M. Scarpiniti;A. Uncini
半监督学习(半监督学习)是只用部分标记的训练集来学习函数的问题。在已标记数据获取成本高而未标记数据丰富的应用中，它具有相当大的实用价值。二进制分类中的一种SSL方法是受到Vapnik关于转导学习(TL)的工作的启发。它被广泛应用于以支持向量机为基础的学习算法，由此产生了所谓的转换式支持向量机。然而，由此产生的优化问题是高度非凸且复杂的。在本文中，我们提出了一种基于TL理论的半监督训练算法，即半监督随机向量函数链(RVFL)网络，它能够在得到标准的凸优化问题的同时获得最先进的性能。特别地，我们证明了，由于RVFL网络的特性，所得到的优化问题可以安全地逼近为在多项式时间内可解的标准二次规划问题。大量的实验验证了我们的建议。作为比较，我们还提出了一种基于流形正则化理论的半监督RVFL算法。
Semi-supervised learning (SSL) is the problem of learning a function with only a partially labeled training set. It has considerable practical interest in applications where labeled data is costly to obtain, while unlabeled data is abundant. One approach to SSL in the case of binary classification is inspired by work on transductive learning (TL) by Vapnik. It has been applied prevalently using support vector machines (SVM) as the base learning algorithm, giving rise to the so-called transductive SVM (TR-SVM). The resulting optimization problem, however, is highly non-convex and complex to solve. In this paper, we propose an alternative semi-supervised training algorithm based on the TL theory, namely semi-supervised random vector functional-link (RVFL) network, which is able to obtain state-of-the-art performance, while resulting in a standard convex optimization problem. In particular we show that, thanks to the characteristics of RVFLs networks, the resulting optimization problem can be safely approximated with a standard quadratic programming problem solvable in polynomial time. A wide range of experiments validate our proposal. As a comparison, we also propose a semi-supervised algorithm for RVFLs based on the theory of manifold regularization.