Multilinear Discriminant Analysis

Multilinear Discriminant Analysis (MDA) extends linear discriminant analysis to handle higher-order data (tensors), aiming to improve classification and dimensionality reduction for complex datasets. Current research focuses on developing robust algorithms, such as those incorporating regularization techniques or leveraging tensor decompositions like Tucker decomposition, to address challenges like singularity and computational efficiency in various applications. MDA finds applications in diverse fields, including image analysis, time-series data processing, and plant disease detection, offering improved accuracy and interpretability compared to traditional methods. The development of efficient and globally convergent algorithms for MDA remains a key area of ongoing investigation.