Data enhancement method based on data dimension reduction process

A data dimension reduction and data technology, applied in the information field, can solve problems such as principal components that are difficult to explain physical meaning, and achieve the effect of small error rate and simple and convenient method

Inactive Publication Date: 2021-07-23
北京师范大学珠海校区
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Problems solved by technology

However, due to multiple matrix transformations in PCA dimensionality reduction calculations, it is often difficult to interpret the physical meaning of the extracted principal components

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  • Data enhancement method based on data dimension reduction process
  • Data enhancement method based on data dimension reduction process
  • Data enhancement method based on data dimension reduction process

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[0044] In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained b...

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Abstract

The invention provides a data enhancement method based on a data dimension reduction process. The method is mainly applied to the technical field of information, and comprises: firstly, constructing a data set into a matrix for subsequent data processing operation; when a CUR matrix decomposition method is used for carrying out row selection and column selection on a matrix, using a PCA dimension reduction method for extracting eigenvectors of rows and columns of the matrix, determining correlation according to the eigenvectors and correlation coefficients of the row vectors and the column vectors so as to select the rows and the columns which are high in representativeness to construct the matrix. Meanwhile, compared with a recovery matrix obtained by a conventional CUR, the recovery matrix obtained by the method provided by the invention has the advantages that the error between the recovery matrix obtained by the method and an actual original matrix is smaller, and the dimension reduction result is more accurate.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a method for data enhancement based on data dimensionality reduction. Background technique [0002] The CUR decomposition method is achieved by decomposing a matrix into three matrices C, U and R so that when they are multiplied together they can approach the original matrix. The current feature selection method is to randomly select a small number of rows and columns to obtain a set that can represent its features. Through this collection, the characteristics of the original data can be restored and estimated, making the characteristics easy to interpret and preserving the additional data structure of the data such as sparsity or non-negativity. However, due to the random selection of rows and columns, the retained features are not necessarily the most significant, which makes the matrix restoration error larger. Exceptional PCA principal component analysis, as a popular ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/2135
Inventor 骆宗伟周梓睿马思嘉侯彦丞
Owner 北京师范大学珠海校区
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