基于权重的多视角全局和局部结构风险最小化分类器

Weight-Based Multi-view Global and Local Structural Risk Minimization Classifier

为了克服传统的多视角分类器无法充分最小化结构风险的不足,提出了基于权重的多视角全局和局部结构风险最小化分类器。该分类器利用特征和视角的权重,使得分类器更符合数据集的分布,从而提高分类器的性能,更有利于最小化结构风险。在Mfeat、Reuters、Corel 3个多视角数据集上的实验表明,通过引入某一数据集中每个样本的视角和特征权重,可以使得该分类器对数据集的分类性能更好。

Minimizing empirical risk,i.e.,training error,means that a classifier should have a high classification accuracy on label-known samples/training samples,while minimizing Vapnik-Chemonenkis (VC) dimension or complexity means that a classifier should have a low prediction error on label-unknown samples/test samples.Since structural risk includes empirical risk and complexity,it can be utilized to measure the effectiveness of a classifier.The structural risk minimization (SRM) means that a classifier should have better classification accuracy on training samples and better prediction performance on test samples simultaneously.Since a global space can be divided into several local ones,the minimization problem of structural risk has global and local minimization.In recent years,multiview classifiers have been developed to process multi-view data sets composed of the samples with multiple views. Each view represents the information of data set from a certain field.However,traditional multi-view classifiers cannot minimize the structural risk fully due to ignoring the differences of views and features.Aiming at the above shortcoming,this paper proposes a weight-based multi-view global and local structural risk minimization classifier. This proposed classifier utilizes the weight of features and the weight of views so that the classifier can attain the consistence with the distribution of data set.It is shown from experiment results on three multi-view data sets,Mfeat, Reuters,and Corel that by introducing the weights of features and views,the corresponding multi-view classifier has better classification performance.Hence,the proposed method can enhance the performances of classifiers and effectively minimize structural risk.