Design method of elastic network constraint self-interpretation sparse representation classifier

A design method and sparse representation technology, applied in the field of pattern recognition, can solve the problems of large fitting errors and low accuracy of classifiers

Pending Publication Date: 2016-12-21
CHINA UNIV OF PETROLEUM (EAST CHINA)
View PDF5 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] Aiming at the above-mentioned shortcomings of large fitting error and low precision in the classifier designed by the existing classifier de

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Design method of elastic network constraint self-interpretation sparse representation classifier
  • Design method of elastic network constraint self-interpretation sparse representation classifier
  • Design method of elastic network constraint self-interpretation sparse representation classifier

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0055] The present invention will be further described below in conjunction with a simulation example and in conjunction with the accompanying drawings.

[0056] A design method for a design method of an elastic network constrained self-explanatory sparse representation classifier, comprising the following steps:

[0057] Step 1: see figure 1 , to design a classifier, the steps are:

[0058] (1) Read the training samples, the training samples have a total of C classes, define X=[X 1 ,X 2 ,...,X c ,...,X C ]∈R D×N Indicates the training samples, D is the face feature dimension, N is the total number of training samples, X 1 ,X 2 ,...,X c ,...,X C respectively represent the 1st, 2nd,...,c,...,C class samples, define N 1 ,N 2 ,...,N c ,...,N C Respectively represent the number of training samples of each type, then N=N 1 +N+,…+N c +…+N C ;

[0059] (2) Carry out two-norm normalization to the training samples to obtain normalized training samples;

[0060] (3) Ta...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a design method of an elastic network constraint self-interpretation sparse representation classifier. The method comprises the following steps: training samples are read, the training samples are linearly transformed to a high-dimensional kernel space, each type of the training samples are learnt in the high-dimensional space, a contribution (i.e., a weight) made by each individual in the type of the training sample to constructing a sub-space of the type of the training samples is found, and a dictionary is constructed by a product of the type of the training samples and a weight matrix; and elastic network coefficient coding of test samples in the kernel space is obtained through training the obtained sparse representation dictionaries, and finally, the test samples are fitted by use of each type of the dictionaries and the elastic network sparse coding corresponding to the dictionaries, fitting errors are calculated, and the type of minimum fitting errors are the type of the test samples. According to the invention, the method is integrated with the advantages of ridge regression and lasso regression, sparse coding features of the samples are enabled to sparse, the fitting errors are also quite small, classification errors are effectively reduced, and the identification performance of a classifier is improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a design method of an elastic network constraint self-explanatory sparse representation classifier. Background technique [0002] Classifier design (Classifier Design) is an important research branch in the field of pattern recognition. Feature extraction is an important part of the pattern recognition system and a prerequisite for pattern classification and recognition. However, how to classify the extracted features to the maximum extent requires It is the ultimate goal of pattern recognition and the core unit of pattern recognition system. From the perspective of classification decision-making, effective classification and discrimination rules are the main factors to reduce the false recognition rate and improve the accuracy of pattern recognition. [0003] At present, the main classifier design methods are as follows. [0004] 1. Support vector machi...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 王立刘宝弟韩丽莎王延江
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap