中文摘要：

将数据集进行合理的维数约简,对于提高一些机器学习算法的效率起着至关重要的影响.本文提出了一种自适应全局—局部集成判别分析算法（Adaptive integrated global and local discriminant analysis,AIGLD）.AILGD利用数据集的全局判别结构和局部判别结构,将线性判别算法（Linear Discriminant Analysis,LDA）与提出的局部判别算法自适应的相结合.在UCI数据库及标准人脸数据库上的识别实验证明,相比于现有算法,AIGLD具有更高的识别准确率及更强的鲁棒性.

英文摘要：

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 major 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 localstructures of data sets together.Inthispaper,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 pro-posed.This method is much more efficient than the classical method for parameters selections,namely cross