Image classification method based on semi-supervised weighted migration discriminant analysis

Abstract

The invention discloses an image classification method based on semi-supervised weighted migration discriminant analysis, and belongs to the technical field of machine learning. According to the method, difference weights of samples are introduced on the basis of a traditional feature migration method, a feature migration classification model is constructed, and the problem of image cross-domain classification is solved. The method comprises the following steps: firstly, designing a cross-domain mean approximation weight; secondly, the cross-domain mean approximation weight is introduced into the maximum mean difference measurement MMD, and the joint distribution adjustment JDA is improved and transformed; thirdly, adding semi-supervised discriminant analysis SDA, fully mining the label information and original space structure information of data, and improving the category separability of the algorithm. According to the method, high-quality shared features between the fields can be effectively extracted, the migration efficiency of knowledge between the fields is improved, and higher classification precision is obtained.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an image classification method based on semi-supervised weighted migration discriminant analysis. Background technique [0002] In the prior art, transfer learning using machine learning methods has been extensively studied. In the face of the rapid development of big data technology and the promotion and application of mobile Internet, 5G network and high-definition cameras and other equipment, human beings receive high-dimensional visual data showing exponential growth. How to quickly and efficiently mine the valuable information of such data has become an important challenge. . Feature extraction converts the high-dimensional feature space into the corresponding low-dimensional representation space through transformation, and maintains the discriminative information of the original space as much as possible in the low-dimensional space. It can avoid the disaster of d...

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Problems solved by technology

[0006] The object of the present invention is to provide an image classification method based on semi-supervised weighted transfer discriminant analysis, which solves the problem that when the training and test samples come from different distributions, the traditional feature transfer method ignores the sample difference when measuring the distribution difference between fields, and the sample Insufficient mining of label information and original structure information, which seriously affects the classification performance

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