Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Nucleation classifier based on local spline embedding

A local spline embedding and classifier technology, which is applied in the direction of instruments, computer components, character and pattern recognition, etc., to achieve the effect of facilitating processing and improving accuracy

Inactive Publication Date: 2016-10-12
YANGZHOU UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, no researchers have proposed a nonlinear classifier based on local spline embedding

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
  • Nucleation classifier based on local spline embedding
  • Nucleation classifier based on local spline embedding
  • Nucleation classifier based on local spline embedding

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] Main technical idea of ​​the present invention is:

[0025] The present invention adopts the non-linear dimension reduction algorithm based on local spline embedding plus the linear SVM (linear support vector machine) classification algorithm to carry out dimension reduction classification to high-dimensional labeled data, and integrates supervisory information to overcome the local spline embedding algorithm The result of high-dimensional information dimensionality reduction is not necessarily conducive to the defects of classification processing. At the same time, constructing intra-class graphs and inter-class graphs to distinguish intra-class neighbors from inter-class neighbors is extremely effective in achieving intra-class compactness and inter-class discreteness. Great help, especially the introduction of regenerating kernel Hilbert spaces, using kernel methods to find the best nonlinear embedding of test data, and being able to handle classification problems tha...

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 nucleation classifier based on local spline embedding. Training data and test data are selected; the dimension of the training data is reduced in a nonlinear way based on local spline embedding; the expanded form of the test data is deduced using a kernel method according to the obtained optimal nonlinear embedding of the training data, namely, nonlinear embedding of the test data on a low-dimensional manifold is obtained; and the dimension-reduced test data is classified using a linear support vector machine (SVM) algorithm. The invention overcomes the defect that good classification performance cannot be achieved for nonlinear classification. According to the invention, the dimension of high-dimensional tagged data is reduced using a nonlinear dimension reduction algorithm based on local spline embedding, and the features of the high-dimensional tagged data are extracted; then, new high-dimensional test data without tags is embedded; and finally, the new test data is classified using the SVM algorithm according to the characteristics of the data.

Description

technical field [0001] The invention is applied to the classification and analysis of high-dimensional data, and in particular relates to a kernelized classifier based on local spline embedding. Background technique [0002] The local spline embedding algorithm is an excellent manifold dimensionality reduction algorithm, but its main purpose is to reduce the dimensionality of data, so this leads to its dimensionality reduction results not necessarily conducive to data classification. [0003] Before the present invention was proposed, the most relevant work of the present invention was a linear classification method based on local spline embedding proposed by the inventor. This method combines the local spline embedding algorithm with the linear discriminant algorithm to find the minimum The reconstruction error of the training data in the global low-dimensional coordinates and the best linear mapping with the best local class discrimination are applied to the test data, and...

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
CPCG06F18/2451G06F18/2411
Inventor 何萍敬田禹徐晓华林惠惠
Owner YANGZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com