协作式整体和局部的分类机

A Collaborative Classifier for Local and Global Learning

提出了一种协作式整体局部分类算法,即C2M(Collaborative classification machine with local and global information),该算法利用两类样本各自的协方差作为整体方向信息,获得两个带整体和局部信息的分类面,并通过组合分类器的平均规则将两个分类面组合,得到最终的最优判决平面.该算法可用两次QP(Quadratic programming)求解,时间复杂度为O(2N3),大大小于M4(Maxi-min margin machine)的O(N4),线性核时的分类精度高于只利用了局部信息的支持向量机(Support vector machine,SVM).理论上证明了在交遇区较多时,C2M可以比M4更有效地利用全局信息,并提出了判断整体信息对分类是否有贡献的4个判别指标.模拟数据和标准数据集上与M4和SVM的对比实验证明了该算法的有效性.

Inspired by covariance matrix stating data direction globally,we construct a novel large margin classifier called collaborative classification machine with local and global information（C2M）.By the median rule of combining classifiers,this model collaboratively learns the decision boundary from two hyperplanes with global information.The proposed C2M algorithm can be individually solved as a quadratic programming（QP） problem,and has O（2N3） time complexity that is faster than O（N4） of existing maxi-min margin machine（M4）.We describe the C2M model definition,provide the geometrical interpretation,and present theoretical justifications.As a major contribution,we show that C2M can robustly utilize the global information when M4 loses the global information on those data sets with confused classes margin.We also exploit kernelization trick and extend C2M to nonlinear classification.Moreover,we show that C2M can be transformed into standard support vector machine（SVM） model and can be solved by other quick algorithms widely used by SVM.Furthermore,we propose four indicators to evaluate the global impact of covariance matrix on classification.Experiments on toy and real-world data sets demonstrate that the C2M has comparable performance with SVM that utilizes only local information,while the C2M is more robust and time saving than M4.