Image data dimension reduction method based on two-dimensional kernel entropy component analysis

Abstract

The invention provides an image data dimension reduction method based on two-dimensional kernel entropy component analysis. The method comprises the following steps that (1) image data are read in; (2) a kernel function is estimated through a Parzen window; (3) a kernel matrix for calculating all the image data in row is set; (4) the eigenvalue and eigenvector of a correlation matrix of the image data are calculated; (5) the Renyi entropy of the image data is calculated; (6) the eigenvector of the correlation matrix of the image data is mapped through a two-dimensional kernel entropy component analysis method, and data dimension reduction is achieved. According to the method, through the two-dimensional analysis method, kernel conversion is directly carried out on the rows or lines of an image, the entropy estimated by the kernel matrix of the image data is sorted, the intrinsic dimension of the image data obtained after dimension reduction is obtained, and the space structure information of the image data can be kept. According to the method, due to the fact that the kernel matrix directly calculates the image data in row or in line, two-dimensional image data do not need to be converted into one-dimensional vectors, and calculation complexity is reduced when the correlation matrix is obtained through kernel conversion.

Description

Technical field [0001] The invention relates to a two-dimensional kernel entropy component analysis (KECA) image data dimensionality reduction method, which belongs to the high-dimensional image data processing method and application technology field, and is suitable for the study of high-dimensional image data dimensionality reduction theory and application technology. Background technique [0002] In applications such as face recognition, digital recognition, and medical image recognition, due to the high-dimensionality of image data, it is often necessary to perform dimensionality reduction first. Image data is the gray value of each pixel expressed in numerical values, which can effectively represent the information of the image and retain the spatial structure information of the image data. However, the image data has a relatively high dimension and a large amount of data, so how to obtain it effectively For important information, reducing the dimensionality of image data an...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to propose a dimensionality reduction method for image data based on two-dimensional kernel entropy component analysis for the existing image data dimensionality reduction methods that cannot effectively utilize the spatial structure information of image data and have high computational complexity.

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