One-class classification algorithm based on manifold learning and its application to imbalanced acoustic target recognition

Imbalanced data is one of the aspects that influence the performance of classification algorithm.Low- dimensional manifold embedded in acoustic signal spectrum was explored and a one-class classification algorithm was proposed based on manifold learning.This one-class classification algorithm recognizes the positive class target according to the error between the manifolds of the input sample and the positive class.This method was applied to acoustic target recognition problem with imbalanced data to verify its effectiveness.The experimental results show that,in comparison with other three one-class classification algorithms,this method can recognize the special target from multiple targets and achieve better recognition performance in the imbalanced data problem,and is more robust to the overlapping between the classes.