基于流形正则化的在线半监督极限学习机

在基于流形正则化的半监督极限学习机(SS-ELM)的基础上,利用分块矩阵的运算法则,提出了在线半监督极限学习机(OSS-ELM)方法.为避免在实时学习的过程中由于数据累积引起的内存不足,通过对SS-ELM的目标函数的流形正则项的近似,给出了OSS-ELM的近似算法OSSELM(buffer).在Abalone数据集上的实验显示,OSS-ELM(buffer)在线学习的累计时间与所处理的样本个数呈线性关系,同时,9个公共数据集上的实验表明,OSS-ELM(buffer)的泛化能力与SS-ELM的泛化能力的相对偏差在1%以下.这些实验结果说明,OSS-ELM(buffer)不仅解决了内存问题,还在基本保持SS-ELM泛化能力的基础上大幅度提高了在线学习速度,可以有效应用于在线半监督学习当中.

Online Semi-Supervised Extreme Learning Machine Based on Manifold Regularization

Abstract： In this paper, with the help of the rules of block matrix multiplication, an online semi-supervised extreme learning machine(OSS ELM) was proposed according to semi-supervised extreme learning machine (SS-ELM) based on manifold regularization.By the analysis of the manifoldregularization term of the objective function of SS ELM, a kind of approximation algorithm of OSS-ELM named OSS-ELM(buffer) was proposed to avoid running out of memory in the process of online learning.The linear relationship between the sample number and the cumulative running time of the OSS-ELM(buffer) was revealed in the experiments using Abalone and the relative deviation of the generalization ability of the OSS ELM and the SS-ELM is less than 1% in 9 public data sets, which show that the OSS ELM(buffer) not only solves the problem of limited memory, but also improves the speed of online learning while keeping the generalization ability of SS ELM. This proves that the OSS ELM(buffer)can be effectively applied to online semi supervised learning.