Robust learning model and image classification system

Abstract

The invention discloses a robust learning model and an image classification system. The robust learning model comprises the steps that a training set is initialized so that an initial category tag matrix is obtained, and training samples in the training set include the samples of which the categories are known with calibration of category tags corresponding to the categories and the samples of which the categories are unknown without calibration of the category tags; the training samples are processed by a construction method based on neighboring definition and reconstruction weights, a reconstruction coefficient matrix is constructed according to similarity between the samples, and symmetrization and normalization processing is performed; soft tags without calibration samples are determined by utilizing the reconstruction coefficient matrix and the initial category tag matrix, and l2,1 normal regularization is performed on the soft tags of the training samples by adopting an iteration method so that a projection matrix and a soft tag matrix are obtained; mapping is performed on samples under test by utilizing the projection matrix so that the soft tags of the samples are obtained; and the samples under test are the samples of which the categories are unknown without calibration of the categories. Influence of mixed signals in an original space can be effectively reduced by the model so that classification accuracy can be enhanced.

Description

technical field [0001] The invention relates to the technical fields of pattern recognition and data mining, and more specifically relates to a robust learning model and image classification system. Background technique [0002] With the continuous development of computer technology and intelligence, image classification technology has become one of the most important research topics in the fields of data mining and machine learning. The classification technology of image classification technology is mainly used to judge the category of unknown data, which is of great significance in the fields of medical data analysis, text, webpage and credit card rating. Therefore, putting accurate classification technology into use can bring huge benefits. social and economic benefits. Many studies have proved that the performance of supervised learning methods is significantly better than that of unsupervised learning methods, but in the real world, supervised data for supervised learn...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a robust learning model and image classification system to solve the problem of low accuracy of classification results of unlabeled samples due to the influence of mixed signals in the original space in the prior art

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