基于相似学习的多类半监督Boosting

J. Tanha, M. Saberian, M. Someren
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引用次数: 6

摘要

本文研究多类半监督分类问题。提出了一种直接解决多类问题的提升算法。所提出的多类方法使用了一个新的多类损失函数,它包含两个项。第一项是多类边际的成本,第二项是未标记数据上的正则化项。正则化项用于最小化对相似度与分类器预测之间的不一致。它根据未标记和标记的样本之间的相似度加权分配软标签。然后,我们使用坐标梯度下降从所提出的损失函数中推导出一种名为CD-MSSBoost的增强算法。该算法进一步用于学习给定数据的最优相似函数。我们在许多UCI数据集上的实验表明,CD-MSSBoost在多类半监督学习方面优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiclass Semi-Supervised Boosting Using Similarity Learning
In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly. The proposed multiclass approach uses a new multiclass loss function, which includes two terms. The first term is the cost of the multiclass margin and the second term is a regularization term on unlabeled data. The regularization term is used to minimize the inconsistency between the pair wise similarity and the classifier predictions. It assigns the soft labels weighted with the similarity between unlabeled and labeled examples. We then derive a boosting algorithm, named CD-MSSBoost, from the proposed loss function using coordinate gradient descent. The derived algorithm is further used for learning optimal similarity function for a given data. Our experiments on a number of UCI datasets show that CD-MSSBoost outperforms the state-of-the-art methods to multiclass semi-supervised learning.
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