Robust Incremental LDA Learning by Autonomous Outlier Detection

Bringing robustness into subspace methods is very important, for training as well as for recognition. In case of Linear Discriminant Analysis (LDA) the task of robust classification is already solved,
therefore, we focus on treating pixel outliers and occlusions in the training stage. More precisely, in this work we consider the task of incremental learning. Based on an augmented LDA basis that incorporates a certain amount of reconstructive information we are able to achieve the desired robustness. The advantage of the enriched basis is that it contains enough reconstructive information to handle noisy data, while it still exploits its full discriminative property.