基于局部子域的最大间距判别分析

Local sub-domains based maximum margin criterion

线性判别分析(LDA)作为一种经典的特征提取方法被广泛地加以研究和运用,然而LDA作为全局判别准则在一定程度上忽视了样本空间的局部结构和局部信息.为此,通过引入局部加权均值(LWM)并结合最大间距判别分析(MMC)提出了具有一定局部学习能力的有监督的特征提取方法—–基于局部加权均值的最大间距判别分析(LBMMC).算法结合了QR分解技术,提高了其执行效率,并通过在数据集上的测试结果表明了该算法的有效性.

Linear discrimination analysis（LDA） as a classic feature extraction method is widely studied and used. However, LDA as a global criterion is neglected to some extent sample space inner local structure and local information. Therefore, when combined with local weighted mean（LWM） and maximum margin criterion（MMC）, a supervised feature extraction method of local learning ability, known as local sub-domains based maximum margin criterion（LBMMC）, is proposed. The method is also combined with the QR decomposition technique to improve the efficiency of the algorithm. Finally, the test on the datasets show the effectiveness of the proposed method.