Dissertation > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Automated reasoning,machine learning

Improvement and Implementation of Manifold Learning Algorithms ISOMAP

Author SunLiPing
Tutor MaHongLian
School Dalian University of Technology
Course Applied Computer Technology
Keywords Dimension of simplicity Manifold Learning Isometric Mapping Sector punctuation
Type Master's thesis
Year 2010
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Dimension Simplicity is an effective means to deal with these high-dimensional data , and can effectively avoid the \Its purpose : the premise does not change the essence of the original high -dimensional data structure to minimize or remove the redundant information to reduce the dimensionality of the original data , so as to achieve the purpose of simplicity dimension . Through the existing the linear simple method of analysis, able to draw the real geometry of linear high-dimensional data . Most of the data in the real world is nonlinear , we need to be able to effectively deal with the nonlinear high-dimensional data dimension simple method , traditional linear simple method , however , the linear nature of the decision , such methods can only be used to find global linear structure in high-dimensional data , can not find the nonlinear structure in high-dimensional data . In this context , the manifold learning methods came into being used to solve the existing problems in the analysis of nonlinear high -dimensional data can effectively find the internal geometry of nonlinear high-dimensional data . Isometric feature mapping algorithm is a typical global optimization algorithm of manifold learning the embedded results can reflect the distance between the manifold in high - dimensional data samples , able to get the ideal embedding results . An important issue in the algorithm is the computation time required . To address this issue , this paper presents a fuzzy C - means clustering method to select representative sector punctuation to improve isometric feature mapping algorithm . First using fuzzy clustering algorithm simple sample set of high-dimensional data obtained cluster centers of the various types of sample points as a landmark isometric mapping algorithm to construct distance matrix , last LMDS method for solving the final result of the embedding . , ISOMAP algorithm whether accurate results of the low- dimensional embedding high -dimensional data sets , mainly depends on the selection of the number of points in the neighborhood , and how to select the appropriate points in the neighborhood , is still an open question . Combination of fuzzy clustering theory and graph theory , the proposed tentative neighborhood value estimation algorithm TNVE, to determine the the ISOMAP algorithm parameters - neighborhood values ??.

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