Multitask machine learning method and multitask machine learning device both used for image classification

Abstract

The invention discloses a multitask machine learning method and a multitask machine learning device both used for image classification. The method and the device are characterized in that low rank approximation of a residual structure and a covariance matrix of a regression matrix are utilized simultaneously, probability modeling is performed on the residual structure, the regression matrix, a low rank decomposition of the regression matrix and the covariance matrix of the regression matrix, learning of parameters of a probability model is performed through a variational deduction method or a sampling method, and a regression matrix high in accuracy is acquired finally and used for image classification. By the scheme, on one side, correlativity information among multitasks in the residual structure is utilized, so that parameter learning accuracy can be improved to improve classification accuracy; on the other side, by performing low rank approximation on the covariance matrix of the regression matrix, calculating complexity of an algorithm can be effectively lowered.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a multi-task machine learning method for image classification, in particular to a multi-task machine learning method for image classification by utilizing a residual structure and a regression matrix covariance matrix low-rank approximation. Background technique [0002] With the advent of the era of big data, massive data mining is particularly important. In massive data mining, how to use the information mined from existing data to guide the mining of new data has become a new research hotspot. Especially when the number of samples for certain tasks is small, using multi-task learning can effectively reduce the time cost of massive data mining and improve the accuracy of information acquisition. For example, in image data classification tasks, using the correlation between different classification tasks of image data can improve the classification accuracy of the task; ...

AI Technical Summary

Problems solved by technology

At the same time, since the dimensions of the covariance matrix Σ and Ω may be large, if they cannot be effectively approximated, the complexity of the multi-task machine learning algorithm may be too large

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