A data reduction method based on a stack noise reduction self-coding neural network

Abstract

The invention discloses a data reduction method based on a stack type noise reduction self-coding neural network, which is characterized in that a reduction model of the stack type noise reduction self-coding neural network is constructed by the following steps of: 1, taking the output of the previous DAE as the input of the next DAE so as to achieve the purpose of layer-by-layer coding; Step 2, representing an original input sample by using a formula shown in the specification, and representing the coding condition of the DAE of the i layer d by the formula shown in the specification,and obtaining the coding condition of the DAE of each layer; And carrying out greedy training and fine tuning layer by layer, and in the fine tuning process, adjusting a cross entropy function of an initial parameter through a BP algorithm to ensure the minimization of a reconstruction error. Improved method-stack type noise reduction self-coding neural network algorithm adopting noise reduction self-coding network The is used for carrying out dimensionality reduction on a sample feature set, so that the complexity of various models is reduced, the classification effect of a classifier in machine learning application is improved, the operation cost of various learning algorithms is reduced, and the feasibility and high efficiency of reduction of the method are verified.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a data reduction method based on a stacked noise reduction autoencoder neural network. Background technique [0002] Autoencoder (Autoencoder, AE) was proposed by Hinton in 2006, and its structure is divided into input layer, output layer, and hidden layer. The number of neurons in the input layer and the output layer is the same, and the number of neurons in the hidden layer is small. The input layer and the hidden layer constitute the encoding network part, and AE compresses data in the encoding network part. [0003] At present, with the rapid development of data acquisition and storage technology, the problem of data redundancy is becoming more and more serious. It not only wastes storage space greatly, but also significantly reduces data-based modeling. [0004] In view of the characteristics of high dimensionality, high redundancy and strong correlation between ind...

AI Technical Summary

Problems solved by technology

[0003] At present, with the rapid development of data acquisition and storage technology, the problem of data redundancy is becoming more and more serious. It not only wastes storage space greatly, but also significantly reduces the cost of data-based modeling.

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