Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
Learning a Propagable Graph for Semisupervised Learning: Classification and Regression
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DOI：
10.1109/tkde.2010.209
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发表时间：
2012-01-01
影响因子：
8.9
通讯作者：
Kassim, Ashraf A.
中科院分区：
文献类型：
--
作者：
Ni, Bingbing;Yan, Shuicheng;Kassim, Ashraf A.
在本文中，我们提出了一个新的框架，称为学习的传播性，两个基本的数据挖掘任务，即，分类和回归。整个学习过程由数据标签和最佳特征表示共同构成调和系统的哲学驱动，其中数据标签相对于基于最佳特征表示构建的相似性图上的传播是不变的。基于这一思想，提出了一个统一的框架，学习的传播性的分类和回归的目的。该框架具有三个特点：1）将标签传播和最优特征表示的追求统一起来，利用最优特征表示代替原始特征表示构造的精细相似图，增强了标签传播过程; 2）统一了分类和回归任务中监督学习和半监督学习的公式; 3）可直接处理多类分类问题。UCI玩具数据集的分类任务，手写数字识别，人脸识别，和微阵列识别以及FG-NET老化数据库上的人类年龄估计的回归任务的广泛实验，都验证了我们提出的学习框架的有效性，与最先进的同行相比。
In this paper, we present a novel framework, called learning by propagability, for two essential data mining tasks, i.e., classification and regression. The whole learning process is driven by the philosophy that the data labels and the optimal feature representation jointly constitute a harmonic system, where the data labels are invariant with respect to the propagation on the similarity graph constructed based on the optimal feature representation. Based on this philosophy, a unified framework of learning by propagability is proposed for the purposes of both classification and regression. Specifically, this framework has three characteristics: 1) the formulation unifies the label propagation and optimal feature representation pursuing, and thus the label propagation process is enhanced by benefiting from the refined similarity graph constructed with the derived optimal feature representation instead of the original representation; 2) it unifies the formulations for supervised and semisupervised learning in both classification and regression tasks; and 3) it can directly deal with the multiclass classification problems. Extensive experiments for the classification task on UCI toy data sets, handwritten digit recognition, face recognition, and microarray recognition as well as for the regression task of human age estimation on the FG-NET aging database, all validate the effectiveness of our proposed learning framework, compared with the state-of-the-art counterparts.