Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines
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发表时间：
2013-06
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通讯作者：
Kohei Ogawa;Motoki Imamura;I. Takeuchi;Masashi Sugiyama
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作者：
Kohei Ogawa;Motoki Imamura;I. Takeuchi;Masashi Sugiyama
半监督支持向量机（S3VM）是一种基于标记和未标记数据的最大间隔分类算法。训练S3VM涉及组合或非凸优化问题，因此在实践中找到全局最优解是棘手的。已经证明，成功找到S3VM的良好（局部）解的关键是逐渐增加未标记数据的效果，即退火。然而，现有的算法遭受退火步骤的分辨率和计算成本之间的权衡。在本文中，我们超越了这种权衡，提出了一种新的训练算法，有效地执行退火与无穷小的分辨率。通过实验，我们表明，所提出的无穷小退火算法往往产生更好的解决方案，更少的计算时间比现有的方法。
The semi-supervised support vector machine (S3VM) is a maximum-margin classification algorithm based on both labeled and unlabeled data. Training S3VM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice. It has been demonstrated that a key to successfully find a good (local) solution of S3VM is to gradually increase the effect of unlabeled data, a la annealing. However, existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost. In this paper, we go beyond this trade-off by proposing a novel training algorithm that efficiently performs annealing with an infinitesimal resolution. Through experiments, we demonstrate that the proposed infinitesimal annealing algorithm tends to produce better solutions with less computation time than existing approaches.