南京大学学报（自然科学版）Issue(4)：517-525,9.DOI:10.13232/j.cnki.jnju.2014.04.016
自适应全局-局部集成判别分析
Adaptive integrated global and local discriminant analysis
摘要
Abstract
In computer vision and information retrieval fields,many applications,such as appearance-based image recognition, often confront high-dimensional data samples.The curse of high dimensionality is usually a maj or cause of limitations of many machine learning algorithms.Hence,it is desired to consider methods of feature extraction (or dimensionality reduction)which are able to find the low-dimensional and compact representations for the high-dimensional data points.The subspace learning algorithm is one of the most popular feature extraction methods.Supervised subspace learning algorithms usually achieve better performances than unsupervised ones.And supervised subspace learning algorithms can be divided into two categories,the global structures based discriminator,such as linear discriminative analysis (LDA),and the local structures based methods,such as marginal Fisher analysis(MFA).From the experiments on image recognition,we can find that the global structures based discriminator and the local structures based discriminator are suitable for different feature extraction tasks.Hence,we hope to seek a discriminative analysis method which can combine the global structures and local-structures of data sets together.In this paper,a new supervised extraction method,called adaptive integrated global and local discriminant analysis (AIGLD),is proposed.The AIGLD algorithm combines the global structure based discriminator (namely,Linear Discriminant Analysis,LDA)with a proposedlocal structure based discriminator together so that it can use both of the global and the local discriminant information of data sets simultaneously.Compared with LDA and the existing local structure based discriminators,AIGLD is capable of feature extraction for different types of data sets.Moreover,an adaptive method for choosing the parameter which is used the balance the effect of local and global discriminators has also been proposed.This method is much more efficient than the classical method for parameters selections,namely cross validation.The efficiency of the proposed algorithm is demonstrated by extensive experiments using UCI data sets and benchmark face image data sets including ORL database and CMU PIE database.And the experimental results show that IGLD outperforms other classical and state of art algorithms.关键词
人脸识别/维数约简/全局结构/局部结构Key words
face recognition/dimensionality reduction/global structure/local structure引用本文复制引用
魏莱..自适应全局-局部集成判别分析[J].南京大学学报（自然科学版）,2014,(4):517-525,9.基金项目
国家自然科学基金(61203240),上海市科研创新项目(14YZ102) （61203240）
