Hybrid learning based on Fisher linear discriminant

Jiawen Gong, Bin Zou, Chen Xu, Jie Xu, Xinge You

Abstract: Hybrid learning is an excellent method that combines the global information of data with the local information of data. Different from the known hybrid learning algorithms, in this paper we propose a new hybrid learning strategy for Fisher Linear Discriminant (FLD) and introduce novel hybrid learning based on FLD (HL-FLD). The main idea of HL-FLD is to obtain the global structural information by FLD firstly, and then use the obtained global information to divide the given data locally in a more detailed way. To study systematically the proposed HL-FLD, we not only establish the generalization bound of HL-FLD and prove that the proposed HL-FLD algorithm is consistent, but also present some discussions on HL-FLD. Since splitting the blocks of a given data is a hyperparameter, we also improve HL-FLD and introduce another new self-adaptive hybrid learning based on FLD (SHL-FLD). The experimental researches for benchmark repository confirm that the proposed two algorithms have better performance in terms of misclassification rates and total time.