Multi View Learning Based On Common Feature Space Projection

Nowdays multi-view analysis is widely used in the fields of computer vision,target detection,human behavior recognition and so on.Multi-view learning method utilizes the compatibility and complementarity of information between different views,which has the higher decision-making performance than single view analysis.However,many conventional multi-view methods just care about the relationship between the pairwise views,but ignore the relationship among all views.This leads to the common space obtained is not discriminative enough.Although some non-pairwise multi-view algorithms gain the discriminative feature space,the global optimal solution may not gain due to the common used generalized eigenvalue decomposition(GEVD)method.Therefore,the research community pays a lot of attentions on developing more efficient multi-view analysis models,which not only can get the discriminative common space,but also have the global optimal solution.This dissertation focuses on 2 multi-view discriminant analysis algorithms:Multi-view Discriminant Analysis for Trace Ratio problem(TRCMv DA)and Harmonic Mean Weighted based Multi-view Discriminant Analysis(HMMv DA).The main contributions are summarized as follows:(1)We propose Trace Ratio Criterion for Multi-view Common Space Discriminant Analysis(TRCMVDA).This supervised method aims to project the samples from multiple views into a common space by considering all views.Because of the TR problem in Multi-view Discriminant Analysis(Mv DA),we present a practical strategy to relieve the restraint condition of positive semi-definite matrix,thus we can obtain the closed form solution by using Newton-Raphson method directly.Compared with the experimental results of 7 mainstream multi-view algorithms on 5 public datasets,the accuracy is improves by more than 3%,and the TR problem can be solved.(2)Some methods give the equal consideration to all between-classes distances,and the close pairwise between-classes tend to overlap in the common space,it will reduce the performance.To avoid this problem,we present Weighted Harmonic Mean Based Multi-view Discriminant Analysis(HMMv DA).This supervised model gives priority to the pairwise between-class distance,then we can get a more discriminative common space,so the different class can be distinguished as much as possible.Moreover,we employ the harmonic mean between-class scatter matrix to increase the robustness of our proposed.A regularization term is introduced to balance the influence of between-class scatter and within-class scatter on the objective function.In the end,we use the Lagrange multiplier method and KKT condition to solve the model iteratively.The experimental results illustrate the accuracy of HMMv DA is slightly higher than TRCMv DA,which demonstrates this model can get more discriminative common space than latter.We present 2 multi-view learning methods,they consider the relationship inter-view and intra-view to gain the discriminant common space,then we can obtain the global optimal solution.This paper also successfully apply our two models to the fields of face recognition and multi-modal human action recognition.The results of our 2 methods prove the universal.