Logistic Label Propagation for Semi-supervised Learning
Logistic Label Propagation for Semi-supervised Learning
复制标题
用于半监督学习的逻辑标签传播
DOI：
10.1007/978-3-642-17537-4_57
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Takumi Kobayashi and Nobuyuki Otsu
中科院分区：
文献类型：
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作者：
Kenji Watanabe;Takumi Kobayashi and Nobuyuki Otsu
标签传播(LP)被用于半监督学习的框架中。本文提出了一种新的物流标签传播方法。该方法使用Logistic函数来准确地估计标签值作为后验概率。在LLP中，利用Logistic函数中的优化系数来有效地估计新输入样本的标签，而不需要像原始LP那样重新计算所有标签值。在分类实验中，该方法在高置信度下比LP和普通Logistic回归产生了更可靠的标签值。此外，即使对一小部分标记样本，LLP的错误率也低于Logistic回归。
Label propagation (LP) is used in the framework of semi-supervised learning. In this paper, we propose a novel method of logistic label propagation (LLP). The proposed method employs logistic functions for accurately estimating the label values as the posterior probabilities. In LLP, the label of newly input sample is efficiently estimated by using the optimized coefficients in the logistic function, without such recomputation of all label values as in original LP. In the experiments on classification, the proposed method produced more reliable label values at the high degree of confidence than LP and ordinary logistic regression. In addition, even for a small portion of the labeled samples, the error rates by LLP were lower than those by the logistic regression.