关键词: SVM-2K模型, 最小二乘损失, 合页损失, 多视角学习

Abstract:

The SVM-2K model is a multi-view learning algorithm using nonsmooth hinge loss.However,the solution process of nonsmooth model is more complex.The LSSVM with smooth least squares loss is introduced as a classical support vector machine algorithm which is widely used in the scientific research field because of its simple calculation,fast operation speed and high precision.In order to improve the training speed of the model,the least square idea is introduced into the SVM-2K.First,the LSSVM-2K model which fully applies the least square loss is proposed.The least square loss is used to replace the hinge loss in the SVM-2K model.The quadratic programming method of the classical multi-view learning model can be replaced by solving the linear equations; second,in order to explore the influence of least squares loss on the SVM-2K model,two other models using least squares loss are proposed,LSSVM-2KI and LSSVM-2KII.In this paper,the new model and other multi-view learning models:SVM₊ (which can be divided into SVM_+A and SVM_+B),MVMED,RMvLSTSVM and SVM-2K are applied to three sets of data sets:animal feature data set (AWA),UCI handwritten digits (Digits) and forest coverage area to test the effectiveness of the new model.Experimental results show that the three new models have a good classification performance.In addition,the LSSVM-2KI model has more advantages in classification accuracy.The LSSVM-2K model not only has a better classification accuracy,but also has great advantages in calculation speed.The LSSVM-2KII model lies between the two in classification effect and training time.

Key words: SVM-2K, least squares loss, hinge loss, multi-view learning