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题名: 面向物联网的海量数据降维算法研究
其他题名: Research on dimension reduction algorithm of mass data for Internet of things
作者: 杨昭
导师: 南琳
分类号: F253.9
关键词: 物联网 ; 海量数据 ; 降维算法
索取号: F253.9/Y29/2011
学位专业: 机械电子工程
学位类别: 硕士
答辩日期: 2011-05-27
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 工业信息学研究室
中文摘要: 物联网是一种连接物体与物体的信息网络。它对物体具有全面的感知能力,对信息具有可靠传送和智能处理的能力。伴随着物联网的应用,可以方便地获得大量的数据。在许多实际应用中,获得的数据是高维的、庞大的、并且数据量可以用“海量”形容。有价值的信息淹没在大规模的海量高维数据中,需要发现数据的内在规律。正是基于这样的背景,对高维数据的降维处理研究是有着重大意义的。 首先通过系统地介绍了物联网的概念、关键技术、实现原理、主要业务以及物联网的海量数据,构建了研究背景。 然后介绍了流形学习的相关概念和基本的预备知识。流形学习是基于局部线性和全局非线性的假设,流形学习方法能有效地探测到非线性数据的内在结构,并且具有保留这些结构的特点。本文对主成分分析、核主成分分析和半正定嵌入算法做了详细的分析、讨论、实现。在核主成分分析和半正定嵌入算法的基础之上提出了基于核的半正定嵌入算法。通过实验表明,基于核的半正定嵌入算法能够捕获更多数据能量,能更好的保持数据内在的流形结构。 最后,利用VC++6.0与matlab7.0混合编程,将降维算法应用在车务通系统中。通过对车务通数据降维处理后,基于核的半正定嵌入算法同样能够捕获数据较多的能量,能够更好的保持数据的内在流形结构。
英文摘要: Internet of Things is an information network to connect objects, with the full capacity to perceive objects, and having the capabilities of reliable transmission and intelligence processing for information. With application of Internet of things, it will generate large amounts of data. The obtained data is high-dimensional and enormous in many practical applications. The valuable information is submerged into large scale dataset. It is necessary to find the intrinsic laws of the dataset. Based on such research background, dimension reduction methods to be handled with high-dimension data is greatly significance. In order to build our research background, the concept, key technologies, and mass data of Internet of Things have been systematically introduced. Then the concept of manifold learning and the basic knowledge are introduced. Based on the assumption of local linearity and global non-linearity, manifold learning methods can explore and preserve the inherent structure of non-linear distributed data. In this paper, principal component analysis algorithm, kernel principal component analysis algorithm and semi definite embedding algorithm are detailed analysis, discussion and implementation. Semi definite embedding algorithm based on kernel space is proposed by semi definite embedding algorithm and kernel principal component analysis algorithm. Finally, experimental results show that semi definite embedding algorithm based on kernel space is able to capture more data energy, and better maintain the intrinsic manifold structure of data. Finally, using VC++6.0 and matlab7.0 mixed programming, the dimension reduction algorithm is applied to car service system。Car service system through the data dimensionality reduction algorithms, semi definite embedding algorithm based on kernel space algorithm can capture more data energy, and better keep the data structure of the internal manifold.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9363
Appears in Collections:工业信息学研究室_学位论文

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Recommended Citation:
杨昭.面向物联网的海量数据降维算法研究.[硕士学位论文].中国科学院沈阳自动化研究所.2011
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