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：半监督学习已广泛应用于从训练数据中估计分类器，此类数据中特征向量的标签并非全部可用。我们提出R语言包gmmsslm，用于在特征向量在每个预定义类别中服从多元高斯（正态）分布时，从这种部分分类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，该框架引入了缺失标签的缺失机制，其中缺失标签的概率通过包含依赖于特征向量熵的协变量的Logistic模型表示。已证明在该框架下，基于对部分分类训练数据拟合的高斯混合模型所形成的贝叶斯分类器，其准确率甚至可能低于基于完全分类样本估计的分类器的错误率。该结论在协方差矩阵相同的两个高斯类别的特定情形下成立。本文聚焦于针对具有任意协方差矩阵的多重高斯类别的算法高效实现，并讨论和阐释了算法初始化策略。该新包在真实数据上进行了演示。