A Width Learning Method Based on Principal Component Analysis

Abstract

The invention relates to the technical field of one-dimensional or two-dimensional data processing, in particular to a data recognition method based on principal component analysis and width learning. In this method, principal component analysis is used to reduce the dimensionality of the original data as the input feature nodes of the width learning network. Further, in order to display the relatively inconspicuous feature nodes in the data after dimension reduction, the width learning is obtained by the linear combination principle of feature nodes. The enhanced nodes input by the network are continuously adjusted based on the accuracy and speed of the training results of the width learning data processing model to improve the width learning training model. Compared with the prior art, the invention has higher recognition accuracy and shorter training time in data processing, and facilitates real-time update of network parameters.

Description

technical field [0001] The invention relates to the technical field of one-dimensional or two-dimensional data processing, in particular to a data recognition method based on principal component analysis and width learning. Background technique [0002] With the development of technology, more and more jobs can be done by computers to improve efficiency. These technologies can be collectively referred to as artificial intelligence. As we all know, deep learning is already one of the important fields of artificial intelligence. Deep learning has been successfully applied in many fields, especially in big data processing, and has achieved unprecedented recognition accuracy. Commonly used deep learning networks are Deep Belief Networks (DBN) [1],[2] , Deep Boltzmann Machines (Deep Boltzmann Machines, DBM) [3] , Convolutional Neural Networks (CNN) [4],[5] . However, the disadvantages of deep learning are becoming increasingly prominent. Deep networks have very complex netw...

AI Technical Summary

Problems solved by technology

[0017] The technical problem to be solved by the present invention is that the structure of the existing deep learning method is complex, the parameter adjustment is uncertain, and the training time is relatively long. Further speaking, the BroadLearning System (broad learning system) proposed by C.L.Philip Chen and Zhulin Liu is Directly inputting the high-dimensional original data into the width learning network will still cause the network structure to be relatively complex and have a large amount of calculation. A width learning method based on principal component analysis is proposed. Data dimensionality reduction is used as the feature node input by the width learning network. In order to further display the relatively inconspicuous feature nodes in the data after dimension reduction, the enhanced node input by the width learning network is obtained by the principle of linear combination of feature nodes. The accuracy and speed of the data processing model training results are used as the basis to continuously adjust the enhancement nodes and improve the width learning training model

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