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Alternative TitleResearch and Application on Subspace Classification
Thesis Advisor刘昌平 ; 黄磊
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword子空间 维数约简 模式分类 人脸识别 图像处理 Subspace Dimensionality Reduction Pattern Classification Face Recognition Image Processing
Abstract当前计算机技术所应用到的很多领域的数据都具有高维的特点,如计算机视觉与图像处理、信息检索的文本分析、数据挖掘和生物特征识别等。从高维观测数据中发掘其中潜在的有意义的低维数据结构,从而得到其紧致的低维表达,具有重要意义。此外,高维数据往往会导致维数灾难的出现。子空间分类,就是利用维数约简技术将高维数据映射到低维空间,从而在低维空间中对数据进行有效地分类。子空间分类对高维数据的内容或语义理解具有重要价值,是模式识别和机器学习领域的一个重要研究方向,具有重要应用价值和理论意义。 本文通过对子空间分类技术进行深入的研究,提出了两种新的维数约简算法,并设计实现了一个应用到人脸识别的分类算法平台系统。本文提出的分类方案依赖模式识别、图像处理、信息处理等技术,利用计算机的强大计算能力,自动完成对算法的测试,具有较高的工作效率和实用价值。 本文工作包括以下几个方面: (1)对现有的子空间分类技术进行了深入研究,详细介绍了多种线性、非线性降维的方法,并分析了各种方法优缺点以及适用的场合。 (2)针对张量学习过程出现的非鲁棒性、不可重现性,本文提出了一种稳定的可重现的张量子空间学习方法,取得了比较好的识别效果,空间复杂度较小,可以初步用于实际应用。 (3)集成鉴别分析中的局部与全局信息,提出一种综合的鉴别矢量投影分析框架,取得了较好的分类效果。 (4)介绍了人脸识别系统的设计思路。
Other AbstractCurrently data of computing technology applied fields are high dimensional, such as computer vision and image processing, text analyzing in information retrieving, data mining and biometric feature recognition. It is of great value to explore potential meaningful low dimensional data structure from high dimensional observation data and then acquire its compact expression. Furthermore, high dimensional data are prone to feature disaster. Subspace classification powerfully processes low dimensional data transformed from high dimensional data by dimensionality reduction technology. Subspace classification is effective in understanding of high dimensional semantics or context, becoming one of the most important topics in pattern recognition and machine learning because of its high value in use and theoretical significance. This dissertation does a lot of research work on subspace classification technology, presents two new algorithms for both linear classification and non-linear classification, and implements an algorithm system that integrate some common methods for face recognition. The classification schemes in this dissertation rely on the technology of pattern recognition, image processing and information processing. The system depending on the powerful computing capability of computers has a good efficiency and practical value. The work of this dissertation includes the following aspects: (1) Doing lots of research work on current technology of subspace classification, introducing the dimensionality reduction methods of both linear and non-linear methods, and analyzing the advantages and faults of these methods. (2) In order to overcome the unrobustness and irreversible shortcomings of tensor projection, the scheme presents a reversible and stable orthogonal tensor projection method in subspace learning, which has a good performance in face recognition and can be used in practice. (3) Proposing a synthesized discriminant projection frame, which integrates both local and global information of each class, thus achieves better results. (4) Relative system designing of face recognition are introduced.
Other Identifier200628014629089
Document Type学位论文
Recommended Citation
GB/T 7714
曾昭雄. 子空间分类研究与应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2009.
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