Migration classification learning method for maintaining sparse structure of image classification

Abstract

The invention discloses a migration classification learning method for maintaining a sparse structure of image classification. The method includes the steps of finding two different source and targetdomains with similar distribution, the source domain containing label data, firstly, training a classification classifier on the source domain by using a supervised classification method, and predicting a pseudo label of target domain data by using the classifier; secondly, constructing edge distribution and conditional distribution terms of the source and target domain data respectively by usingthe maximum mean difference, and combining the both to form a joint distribution term; thirdly, constructing a sparse representation matrix S on all the data by using an effective projection sparse learning toolkit, to construct a sparse structure preserving term; fourthly, constructing a structural risk minimization term by using the structural risk minimization principle; and fifthly, combiningthe structural risk minimization term, the joint distribution term, and the sparse structure preserving term to construct a uniform migration classification learning framework, and substituting into the framework using a classification function representation theorem including a kernel function to obtain a classifier that can be finally used to predict the target domain category.

Description

technical field [0001] The invention relates to the technical field of transfer learning classification in machine learning, in particular to a transfer classification learning method for maintaining the sparse structure of image classification. Background technique [0002] Traditional machine learning has two basic assumptions, one is that the test samples and training samples should satisfy independent and identical distribution, and the other is that there must be enough available training samples. However, these two conditions are often not easy to meet, so transfer learning came out as the times require. Transfer learning is a method of using known content knowledge to solve related but different fields. Use the knowledge already known in a field to solve the problem that there are only a few labeled samples or even no labeled samples in the learning target field. Often, the smaller the difference between the two fields and the more factors they share, the easier and ...

AI Technical Summary

Problems solved by technology

However, since manifold regularization is used to describe local structural information, this regularization framework is not very satisfactory for image classification.

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