Formulae neighborhood based data dimensionality reduction method

Abstract

The invention discloses a data dimension reduction method based on neighborhood rule. The method includes the following steps: firstly, a spherical neighborhood of present sample points is established by using the geometric spherical-modelling theory and all the sample points contained in the spherical neighborhood are adopted as candidate neighbor points, thus not only preserving the effectivity of the dimension reduction capability when data sets are sparse but also getting the advantages of low-sensitivity to isolated points and good stability of the preserved topological structure; then a data relevance matrix more matching semantics can be obtained by relevance measurement based on route clusters to update the candidate neighbor points in the spherical neighborhood and optimize the regular neighborhood space of the present sample points, thus improving the phenomenon that the dimension reduction of sample sets provided with folded curved faces is apt to suffer the integrated-structure distortion in case of heterogeneous data distribution. The experiments on different sample sets demonstrate that the method provided by the invention is available and effective.

Description

technical field [0001] The invention relates to the field of information processing, in particular to a data dimension reduction method based on rule neighborhood. Background technique [0002] Data dimensionality reduction refers to reducing the data in a high-dimensional space to a lower-dimensional space to eliminate the redundancy of the original data and improve the subsequent processing capability of the data. Traditional data dimensionality reduction is mainly based on linearity. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two most widely used linear dimensionality reduction methods. Although these two methods are theoretically mature and computationally fast, they can only reduce the dimensionality of data with a linear structure, and are difficult to directly deal with large-scale, high-dimensional and nonlinear data. [0003] Seung and Lee pointed out in the article "The Manifold Ways of Perception" in the internationally ren...

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Problems solved by technology

However, this method also has some application limitations, such as: it is too sensitive to parameters and external noise, and the dimensionality reduction performance fails when dealing with sparsely distributed data sets.

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