Data dimension reduction method based on data global-local structure preserving projections

A local structure and projection-preserving technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of only focusing on the global data or the loss of local feature data feature information

Inactive Publication Date: 2014-02-26
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the defect that the existing data dimensionality reduction methods only focus on the global or local features of the data and easily cause the loss of data feature information, the present invention provides a data-based global-local structure that can simultaneously mine the global and local features of the data and has a good dimensionality reduction effect Data dimensionality reduction method for maintaining projections (GLSPP, global-local structure preserving projections)

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  • Data dimension reduction method based on data global-local structure preserving projections
  • Data dimension reduction method based on data global-local structure preserving projections
  • Data dimension reduction method based on data global-local structure preserving projections

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[0056] Example: The following two synthetic data, Intersect and Swiss roll, are used as examples to illustrate the superiority of the present invention. The sample sets of Intersect and Swiss roll data are both composed of 1800 sample points. The dimension of the original data space is 3, and the dimension of the data space after dimension reduction is 2. The generating function of Intersect data is: t=2π*[1:n]’ / n, X=[cos(t)0.5*cos(t).*sin(t)50*rand(n,1)]. The generating function of Swiss roll data is: t=3π / 2*(1+2*rand(n,1)), X=[t.*cos(t)300*rand(n,1)t.*sin( t)]. Select PCA and LPP to compare with GLSPP, and use the k-nearest neighbor method to define the neighborhood of data points, and k is set to 15.

[0057] figure 2 and image 3 They are the visualization effects of Intersect and Swiss roll data sample sets reduced to 2D under different algorithms. It can be seen that PCA projects the sample data along the direction of maximum variance, which basically maintains the...

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Abstract

A data dimension reduction method based on data global-local structure preserving projections includes the following steps: 1), creating a sample set and calculating Euclidean distances among sample pairs; 2), diving territory of each sample point to acquire neighboring points and non-neighboring points; 3), creating a neighboring-right matrix and a non-neighboring-right matrix of the sample set; 4), creating target functions corresponding to data global structure preservation and data local structure preservation, and constructing optimization problems; 5), transforming the optimization problems into generalized eigenvalue problems, and creating a projection matrix according to feature vectors acquired by solution; 6), projecting the sample set to obtain dimension reduced data. According to the method, global and local structures of high-dimensional data are maintained during dimension reduction, the dimension reduced data can describe basic features of original high-dimensional data completely and accurately, and accordingly, data dimension reduction effect is improved.

Description

technical field [0001] The invention relates to the fields of machine learning, pattern recognition and artificial intelligence, in particular to a data dimensionality reduction method based on data global-local structure preserving projection. Background technique [0002] Today, benefiting from the rapid development of information technology, data acquisition and storage have become relatively easy. Scientific research, engineering applications, and various fields of social life are rapidly generating massive amounts of data all the time. These data are characterized by diversity, large scale, and high dimensionality. Although they contain rich rules and information, they are often covered up by a large amount of redundant data and are difficult to observe intuitively. How to effectively extract useful feature information or rules from high-dimensional massive data has become a basic problem faced by information science and technology today. [0003] At present, data dime...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 罗利佳包士毅高增梁
Owner ZHEJIANG UNIV OF TECH
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