图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行...图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行近邻搜索,可以有效降低图半监督学习方法所需的构图时间.图像分割实验表明,该方法一方面可以达到更好的分割效果,使分割准确率提高0.47%左右;另一方面可以大幅度减小分割时间,以一幅大小为300像素×800像素的图像为例,分割时间可减少为图半监督学习所需时间的28.5%左右.展开更多
This paper proposes a semi-supervised inductive algorithm adopting a Gaussian random field(GRF)and Gaussian process.We introduce the prior based on graph regularization.This regularization term measures the p-smoothne...This paper proposes a semi-supervised inductive algorithm adopting a Gaussian random field(GRF)and Gaussian process.We introduce the prior based on graph regularization.This regularization term measures the p-smoothness over the graph.A new conditional probability called the extended Bernoulli model(EBM)is also proposed.EBM generalizes the logistic regression to the semi-supervised case,and especially,it can naturally represent the margin.In the training phase,a novel solution is given to the discrete regularization framework defined on the graphs.For the new test data,we present the prediction formulation,and explain how the margin model affects the classification boundary.A hyper-parameter estimation method is also developed.Experimental results show that our method is competitive with the existing semi-supervised inductive and transductive methods.展开更多
文摘图半监督学习(Graph based semi-supervised learning,GSL)方法需要花费大量时间构造一个近邻图,速度比较慢.本文提出了一种哈希图半监督学习(Hash graph based semi-supervised learning,HGSL)方法,该方法通过局部敏感的哈希函数进行近邻搜索,可以有效降低图半监督学习方法所需的构图时间.图像分割实验表明,该方法一方面可以达到更好的分割效果,使分割准确率提高0.47%左右;另一方面可以大幅度减小分割时间,以一幅大小为300像素×800像素的图像为例,分割时间可减少为图半监督学习所需时间的28.5%左右.
基金This work was supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology(TNList).
文摘This paper proposes a semi-supervised inductive algorithm adopting a Gaussian random field(GRF)and Gaussian process.We introduce the prior based on graph regularization.This regularization term measures the p-smoothness over the graph.A new conditional probability called the extended Bernoulli model(EBM)is also proposed.EBM generalizes the logistic regression to the semi-supervised case,and especially,it can naturally represent the margin.In the training phase,a novel solution is given to the discrete regularization framework defined on the graphs.For the new test data,we present the prediction formulation,and explain how the margin model affects the classification boundary.A hyper-parameter estimation method is also developed.Experimental results show that our method is competitive with the existing semi-supervised inductive and transductive methods.
