Semi-supervised learning is being extensively applied to estimate classifiers from training data in which not all the labels of the feature vectors are available. We present gmmsslm, an R package for estimating the Bayes' classifier from such partially classified data in the case where the feature vector has a multivariate Gaussian (normal) distribution in each of the predefined classes. Our package implements a recently proposed Gaussian mixture modelling framework that incorporates a missingness mechanism for the missing labels in which the probability of a missing label is represented via a logistic model with covariates that depend on the entropy of the feature vector. Under this framework, it has been shown that the accuracy of the Bayes' classifier formed from the Gaussian mixture model fitted to the partially classified training data can even have lower error rate than if it were estimated from the sample completely classified. This result was established in the particular case of two Gaussian classes with a common covariance matrix. Here, we focus on the effective implementation of an algorithm for multiple Gaussian classes with arbitrary covariance matrices. A strategy for initialising the algorithm is discussed and illustrated. The new package is demonstrated on some real data.

翻译：半监督学习被广泛用于从训练数据中估计分类器，此时特征向量的标签并非全部可得。我们提出了gmmsslm这个R语言包，用于在特征向量在每个预定义类别中服从多元高斯（正态）分布的情况下，从这类部分标注数据中估计贝叶斯分类器。该包实现了一种近期提出的高斯混合建模框架，该框架为缺失标签引入了一种缺失机制，通过逻辑模型表示标签缺失概率，其协变量依赖于特征向量的熵。研究表明，在此框架下，基于部分标注训练数据拟合的高斯混合模型所构建的贝叶斯分类器，其准确率甚至可能低于完全标注样本估计的分类器的错误率。该结论在协方差矩阵相同的二元高斯类别特例中已得到证明。本文重点聚焦于多类别高斯分布（允许任意协方差矩阵）算法的有效实现，讨论并展示了算法初始化策略，并通过真实数据验证了新包的性能。