Robust Multi-view Classification with Sample Constraints

摘要

This paper proposes a new multi-view classification method by taking three sample constraints into account to automatically assign large weights to important samples and small weights to unimportant samples. To do this, we first demonstrate that different samples have different contributions to the classification models, and then propose to consider sample weight, class weight, and view weight, to overcome the influence of different levels of noise. Specifically, the sample weight for every data point is obtained by penalizing an \(\ell _{2,1}\)-norm loss on its estimation error to reduce the influence of the sample-level noise, the class weight for each class is obtained by considering the misclassification cost as well as imbalance class to overcome the influence of class-level noise, and the view weight for each view is obtained by penalizing a squared root operator on the estimation error of each view to reduce the influence of view-level noise. In particular, our proposed sample constraints can be easily embedded in previous multi-view learning models. Experimental results on simulated and real data sets showed that our proposed method was superior to the state-of-the-art classification methods in terms of classification performance of cost-sensitive learning.