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 inputobjects in a natural form in overcoming small sample size problems and in providing high classificationaccuracy. multi-linear discriminant analysis (mlda) is an efficient classifier, which employs tensorproperties to simplify computation and improve accuracy. in this study, a boosting framework is exploitedto further improve a tensor-based mlda classifier. in the boosting framework, several weak learnersare trained with different distribution of training samples and, then, integrated with suitable weightsto 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 finaldecision and enables the bmlda to act as a multi-class classifier. to assess bmlda, several versionsof 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 thatthe proposed bmlda outperformed its competitors. the proposed method is beneficial in exploiting theboosting framework to accommodate tensor-based learners that totally construct a powerful multi-classensemble classifier with much higher performance.