On incremental semi-supervised discriminant analysis

Abstract

In various pattern classification problems, semi-supervised discriminant analysis has shown its effectiveness in utilizing unlabeled data to yield better performance than linear discriminant analysis. However, many of these semi-supervised classifiers operate in batch-mode and do not allow to incrementally update the existing model, which is one of the major limitations. This paper presents an incremental semi-supervised discriminant analysis algorithm, which utilizes the unlabeled data for enabling incremental learning. The major contributions of this research are (1) utilizing large unlabeled training set to estimate the total scatter matrix, (2) incremental learning approach that requires updating only the between-class scatter matrix and not the total scatter matrix, and (3) utilizing manifold regularization for robust estimation of total variability and sufficient spanning set representation for incremental learning. Using face recognition as the case study, evaluation is performed on the CMU-PIE, CMU-MultiPIE, and NIR-VIS-2.0 datasets. The experimental results show that the incremental model is consistent with the batch counterpart and reduces the training time significantly.