Multivariate Functional Linear Discriminant Analysis for the
  Classification of Short Time Series with Missing Data
Rahul Bordoloi Clémence Réda Orell Trautmann Saptarshi Bej Olaf Wolkenhauer
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.