A multi-task dictionary single classification method, system, device and storage medium

Abstract

This application provides a multi-task dictionary single classification method, including: obtaining tasks to be classified; learning a comprehensive dictionary and an analysis dictionary for each task to be classified; using the dictionary to learn models, analyze non-correlation items, and analyze coefficient coding The extraction item and the multi-task single classification item establish the objective optimization function; among them, the dictionary learning model includes a comprehensive dictionary and an analysis dictionary; the optimization function is solved to obtain a linear classifier and a nonlinear classifier respectively; the linear classifier and the nonlinear classifier are used to treat classification Tasks are categorized. This application uses a task to learn a comprehensive dictionary and an analysis dictionary, and makes the encoding coefficients as sparse as possible for other tasks, which can better represent the underlying structure of the data. At the same time, using the multi-task learning model also greatly reduces the computational complexity. The present application also provides a multi-task dictionary single classification system, a device and a computer-readable storage medium, which have the above-mentioned advantageous 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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