函数型数据的半监督Logistic判别
Semi-supervised logistic discrimination for functional data
摘要：
讨论了基于标记和未标记函数数据集的多类分类方法。我们提出了一个半监督逻辑模型的功能数据分析的背景下进行分类。我们提出的模型中的未知参数估计正则化的帮助下，期望最大化算法。建模过程中的一个关键点是半监督函数逻辑模型中正则化参数的选择。为了选择调整后的参数，我们介绍了模型选择标准，从信息论和贝叶斯的观点。通过蒙特卡罗模拟和实际数据分析，验证了该建模策略的有效性。
英文摘要：
Multi-class classification methods based on both labeled and unlabeled functional data sets are discussed. We present a semi-supervised logistic model for classification in the context of functional data analysis. Unknown parameters in our proposed model are estimated by regularization with the help of EM algorithm. A crucial point in the modeling procedure is the choice of a regularization parameter involved in the semi-supervised functional logistic model. In order to select the adjusted parameter, we introduce model selection criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations and a real data analysis are given to examine the effectiveness of our proposed modeling strategy.
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