半监督极限学习机及其在近红外光谱数据分析中的应用

Semi-supervised extreme learning machine and its application in analysis of near-infrared spectroscopy data

当数据集中包含的训练信息不充分时,监督的极限学习机较难应用,因此将半监督学习应用到极限学习机,提出一种半监督极限学习机分类模型;但其模型是非凸、非光滑的,很难直接求其全局最优解。为此利用组合优化方法,将提出的半监督极限学习机化为线性混合整数规划,可直接得到其全局最优解。进一步,利用近红外光谱技术,将半监督极限学习机应用于药品和杂交种子的近红外光谱数据的模式分类。与传统方法相比,在不同的光谱区域的数值实验结果显示:当数据集中包含训练信息不充分时,提出的半监督极限学习机提高了模型的推广能力,验证了所提出方法的可行性和有效性。

When insufficient training information is available, supervised Extreme Learning Machine（ ELM） is difficult to use. Thus applying semi-supervised learning to ELM, a Semi-Supervised ELM（ SSELM） framework was proposed.However, it is difficult to find the optimal solution of SSELM due to its nonconvexity and nonsmoothness. Using combinatorial optimization method, SSELM was solved by reformulating SSELM as a linear mixed integer program. Furthermore, SSELM was used for the direct recognition of medicine and seeds datasets using Near-Infra Red spectroscopy（ NIR） technology. Compared with the traditional ELM methods, the experimental results show that SSELM can improve the generation when insufficient training information is available, which indicates the feasibility and effectiveness of the proposed method.