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

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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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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