Incremental 2-directional 2-dimensional linear discriminant analysis for multitask pattern recognition

In this paper, we propose an incremental 2-directional 2-dimensional linear discriminant analysis (I-(2D)²LDA) for multitask pattern recognition (MTPR) problems in which a chunk of training data for a particular task are given sequentially and the task is switched to another related task one after another. In I-(2D)²LDA, a discriminant space of the current task spanned by 2 types of discriminant vectors is augmented with effective discriminant vectors that are selected from other tasks based on the class separability. We call the selective augmentation of discriminant vectors knowledge transfer of feature space. In the experiments, the proposed I-(2D)²LDA is evaluated for the three tasks using the ORL face data set: person identification (Task 1), gender recognition (Task 2), and young-senior discrimination (Task 3). The results show that the knowledge transfer works well for Tasks 2 and 3; that is, the test performance of gender recognition and that of young-senior discrimination are enhanced.