Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data

arXiv:2402.13103v1 Announce Type: new
Abstract: 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 …

abstract age analysis arxiv classification cs.lg data dependencies features functional functions incomplete data lda linear math.st multivariate series statistical stat.th time series tool type