Graph model based on multi-view dictionary learning

Abstract

The invention discloses a graph model based on multi-view dictionary learning. The method comprises the steps: carrying out the data dimension reduction and preprocessing of original graph data through principal component analysis and linear discrimination analysis, removing redundant features in the data, and maintaining the high discrimination of the data; learning essential characteristics contained in the data by using a multi-view dictionary learning method, and training to obtain a comprehensive dictionary, an analysis dictionary, sparse codes corresponding to the samples and an SVM linear classifier; inputting the sparse codes of the samples into an SVM classifier, and generating a plurality of prediction labels under different visual angles according to a multi-visual-angle principle after the sparse codes are processed by the classifier; the predicted multi-view labels are integrated through a voting mechanism, and final sample labels are generated and used for calculating model accuracy; the method has the advantages of high information utilization rate, more efficient decision making, high specificity and the like.

Description

technical field [0001] The invention relates to the technical field of graph mining and dictionary learning, in particular to a graph model based on multi-view dictionary learning. Background technique [0002] In recent years, major breakthroughs have been made in the development of deep learning, and it has been widely used in language recognition, object detection, machine translation and other fields, and has demonstrated its powerful feature extraction capabilities. Deep learning has achieved great success on Euclidean data, such as pictures, videos, and speech, because such data have some good properties such as translation invariance, local connectivity, and semantic synthesis of image data , but the data generated from the non-Euclidean domain, such as Graph data and Manifold data, do not have the above-mentioned good properties, and the structure is often very complex, and traditional methods such as convolution cannot directly applied to such data. A large number...

AI Technical Summary

Problems solved by technology

Dictionary learning is widely used in image denoising, super-resolution, compressed sensing and other fields, but so far, there are few or few studies on applying multi-view technology to dictionary learning, and applying dictionary learning to graph data

For the algorithm, this is a vacancy in application and a lack of function; at the same time, the existing technology has shortcomings and deficiencies such as low information utilization rate, lack of function, high consumption of computing resources, and long training period.

Images