当前位置: X-MOL 学术Inform. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Weighted Co-association Rate-based Laplacian Regularized Label Description for Semi-supervised Regression
Information Sciences ( IF 8.1 ) Pub Date : 2020-09-18 , DOI: 10.1016/j.ins.2020.09.015
Jaehong Yu , Youngdoo Son

Smoothness regularization derives the optimal regression function by minimizing the squared loss combined with a smoothness regularizer that restricts the variation of the function within a neighboring region. Thus, the regression function can effectively accommodate intrinsic data structures, and prediction performance can be improved when the label information is insufficient. In this study, we propose a weighted co-association rate-based Laplacian regularized label description algorithm. In the proposed algorithm, we define a regression function by combining weighted co-association rates and a label descriptive function. We use the weighted co-association rate, computed by summarizing various clustering solutions, to depict the data structure. The label descriptive function identifies a latent label distribution, and hence helps the regression function to accurately involve as much true label information as possible. To derive the optimal label descriptive function, we apply the smoothness regularizer to label descriptive function. Experiments were conducted on various benchmark datasets to examine the properties of the proposed algorithms, and the results were compared with those of the existing methods. The experimental results confirm that the proposed algorithm outperforms the previous methods.



中文翻译:

半监督回归的基于加权关联度的拉普拉斯正则化标签描述

平滑度正则化通过最小化平方损失与平滑度正则化器相结合来得出最佳回归函数,该平滑度正则化器将函数的变化限制在相邻区域内。因此,回归函数可以有效地容纳固有数据结构,并且当标签信息不足时可以提高预测性能。在这项研究中,我们提出了一种基于加权关联度的拉普拉斯正则化标签描述算法。在提出的算法中,我们通过结合加权的关联率和标签描述函数来定义回归函数。我们使用通过汇总各种聚类解决方案而计算出的加权协关联率来描述数据结构。标签描述功能可识别潜在的标签分布,从而帮助回归函数准确地包含尽可能多的真实标签信息。为了得出最佳的标签描述函数,我们将平滑度正则化器应用于标签描述函数。在各种基准数据集上进行了实验,以检验所提出算法的性能,并将结果与​​现有方法进行比较。实验结果证明,该算法优于以前的算法。并将结果与​​现有方法进行比较。实验结果表明,该算法优于以前的算法。并将结果与​​现有方法进行比较。实验结果表明,该算法优于以前的算法。

更新日期:2020-09-20
down
wechat
bug