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In the big data era, the widespread of massive multi-modal data has caused huge changes in the data characteristics, namely wide variety and low value density.Different types of data are characterized by both function independently and complement each other.Discovering the hidden value behind multi-modal data has become the key problem in big data mining tasks.Therefore, to tackle the shortcomings of the low-quality multimodal data, this paper proposes a new multimodal robust feature learning method by introducing the modal specific error matrix.The effect of noisy information on the fusion result can thus be effectively reduced.Moreover, a dual graph-regularization mechanism for data manifolds and feature manifolds is designed to describe the spatial structure of multimodal data, which can ensure the data stability during multimodal feature learning.On six real-world multi-modal data sets, the results are compared with several classical algorithms in recent years based on three evaluation indexes, namely accuracy (ACC), normalized mutual information (NMI) and purity (PUR).Experimental results show that the proposed method is superior to all other compared algorithms, especially in network data sets Webkb containing large amounts of noise information, its ACC and NMI are improved by about 10% compared with the baseline algorithms.It can be seen that the proposed algorithm can accurately learn the sharing features of multi-modal data.

Since the traditional semi-supervised multi-view algorithms seldom take into account the diversity of information contained in different views and neglect the consistency of spatial structure between different views, they hardly achieve promising performance when dealing with multi-view data with noise and outlying entries. Although some researchers have proposed semi-supervised multi-view methods,these methods do not make full use of sample discriminant information and subspace structure information under different metric learning,which leads to the unsatisfactory classification results. To deal with the above problems,this paper proposes a semi-supervised multi-view classification via consistency constraint (SMCC) for multi-view data analysis. Firstly, the consistency constraints between different views are enhanced based on the Hilbert-Schmidt independence criteria (HSIC). Then, the dimensionality reduction is performed by feature projection to preserve the local manifold structure, which is integrated with Frobenius norm constraint to improve the robustness of the algorithm. Furthermore, the corresponding weights are adaptively assigned to different views to reduce the influence of feature information and noise pollution in different views. Finally, the proposed model can be solved efficiently using the linear alternative direction method with adaptive penalty and eigen-decomposition. The experimental results on four benchmark datasets show that the proposed algorithm can discover more effective discriminant information from multi-view data and its accuracy is improved.