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.

在大多元和不完整数据时代，需要一个能够计算可行地估计特征之间统计依赖并处理缺失值的方法。我们开发了 MUDRA（多元 FLDA 的多元版本）来解决这个问题，并描述了一种高效的期望 / 条件最大化算法来推断其参数。我们在 “Articulary Word Recognition” 数据集上评估了 MUDRA 的预测能力，并展示了其在处理缺失数据时相对于最先进方法的改进。MUDRA 允许解释性地对具有大量缺失数据的数据集进行分类，这在医学或心理数据集中尤其有用。