Multi-task dictionary sheet classification method, system and device and storage medium

Abstract

The invention provides a multi-task dictionary sheet classification method. The method comprises the steps of obtaining to-be-classified tasks; each to-be-classified task learning a comprehensive dictionary and an analysis dictionary; utilizing adictionary learning model to analyze the non-correlation item, the analysis coefficient code extraction item and the multi-task single classification itemto establish a target optimization function; wherein the dictionary learning model comprises a comprehensive dictionary and an analysis dictionary; solving the optimization function to respectively obtain a linear classifier and a nonlinear classifier; and classifying the to-be-classified tasks by utilizing the linear classifier and the nonlinear classifier. According to the method, one task is adopted to learn one comprehensive dictionary and one analysis dictionary, and the coding coefficient is as sparse as possible for other tasks, so that the potential structure of the data can be betterrepresented. And meanwhile, a multi-task learning model is utilized, so that the calculation complexity is greatly reduced. The invention further provides a multi-task dictionary sheet classificationsystem and device and a computer readable storage medium, which have the above beneficial effects.

Description

technical field [0001] The present application relates to the field of tag classification, and more specifically, relates to a multi-task dictionary single classification method, system, device and storage medium. Background technique [0002] In the prior art, multi-task learning is divided into two categories. The first type is the feature sharing method. By learning the feature subspace, some features shared by all tasks are learned, and then the classifier is learned based on these features. The second category is the parameter sharing method. By assuming that the classification hyperplanes of several related tasks are offset relative to the same central hyperplane, each task can learn more information. In order to fully utilize the information of multiple tasks, the computational complexity increases as the number of tasks increases. [0003] Dictionary learning has been widely used in classification tasks, such as image classification and face recognition. The over-...

AI Technical Summary

Problems solved by technology

Traditional dictionary learning has the following disadvantages: First, in order to ensure the sparsity of the coding coefficients, the constraint items of the coding coefficients often adopt the L0 norm or L1 norm, resulting in a long training time

Second, for classification tasks, dictionary learning often directly uses encoding coefficients to learn classifiers, resulting in high computational complexity

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