多变量函数线性判别分析用于缺失数据的短时间序列分类
Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data
AI总结：
本文提出MUDRA方法，通过高效ECM算法处理多变量时间序列中的缺失数据，提升分类性能，尤其在缺失数据情况下表现更优。
摘要：
函数线性判别分析（FLDA）是一种强大的工具，将LDA介导的多类分类和降维扩展到单变量时间序列函数。然而，在大数据时代，必须以计算上可行的方式估计特征间的统计依赖性，同时处理缺失数据。本文开发了FLDA的多变量版本（MUDRA），描述了高效的期望/条件最大化（ECM）算法来推断其参数。我们在
英文摘要：
Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated multiclass classification and dimension reduction to univariate time-series functions. However, in the age of large multivariate and incomplete data, statistical dependencies between features must be estimated in a computationally tractable way, while also dealing with missing data. There is a need for a computationally tractable approach that considers the statistical dependencies between features and can handle missing values. We here develop a multivariate version of FLDA (MUDRA) to tackle this issue and describe an efficient expectation/conditional-maximization (ECM) algorithm to infer its parameters. We assess its predictive power on the "Articulary Word Recognition" data set and show its improvement over the state-of-the-art, especially in the case of missing data. MUDRA allows interpretable classification of data sets with large proportions of missing data, which will be particularly useful for medical or psychological data sets.
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