论文标题
功能数据聚类通过信息最大化
Functional data clustering via information maximization
论文作者
论文摘要
通过信息最大化提出了一种用于聚类功能数据的新方法。所提出的方法以无监督的方式学习概率分类器，以便最大化数据点和群集分配之间的相互信息（或平方损失共同信息）。这种提出的方法的一个值得注意的优点是，它仅涉及模型参数的连续优化，这比群集分配的离散优化更简单，避免了生成模型的缺点。与某些现有方法不同，所提出的方法不需要估计不同簇下的Karhunen-Lo` eve扩展分数的概率密度，也不需要常见的特征功能假设。通过模拟研究和实际数据分析证明了所提出方法的经验性能和应用。此外，所提出的方法允许样本外聚类，其效果与某些监督分类器的效果相当。
A new method for clustering functional data is proposed via information maximization. The proposed method learns a probabilistic classifier in an unsupervised manner so that mutual information (or squared loss mutual information) between data points and cluster assignments is maximized. A notable advantage of this proposed method is that it only involves continuous optimization of model parameters, which is simpler than discrete optimization of cluster assignments and avoids the disadvantages of generative models. Unlike some existing methods, the proposed method does not require estimating the probability densities of Karhunen-Lo`eve expansion scores under different clusters and also does not require the common eigenfunction assumption. The empirical performance and the applications of the proposed methods are demonstrated by simulation studies and real data analyses. In addition, the proposed method allows for out-of-sample clustering, and its effect is comparable with that of some supervised classifiers.
