Enearest neighbornsemble based multi-linear discriminant analysis with boosting and

The tensor based classifier has attracted a great deal of interest, due to its representation of input objects in a natural form in overcoming small sample size problems and in providing high classification accuracy. Multi-Linear Discriminant Analysis (MLDA) is an efficient classifier, which employs tensor properties to simplify computation and improve accuracy. In this study, a boosting framework is exploited to further improve a tensor-based MLDA classifier. In the boosting framework, several weak learners are trained with different distribution of training samples and, then, integrated with suitable weights to build a strong classifier with a high generalization capacity. In our proposed method, namely BMLDA (Boosted MLDA), the MLDA classifiers are weakened and considered as feature projection components (weak learners) in the boosting framework. Finally, a Nearest Neighbor (NN) classifier makes the final decision and enables the BMLDA to act as a multi-class classifier. To assess BMLDA, several versions of Linear Discriminant Analysis (LDA) classifiers, such as boosted direct LDA, direct LDA, subclass-LDA, MLDA and LDA, were implemented. Empirical evaluations on two real applications demonstrated that the proposed BMLDA outperformed its competitors. The proposed method is beneficial in exploiting the boosting framework to accommodate tensor-based learners that totally construct a powerful multi-class ensemble classifier with much higher performance.