Riemannian manifold maintenance kernel learning method and device based on geodesic distance

A technology of geodesic distance and nuclear learning, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of not being able to effectively expand data applications

Active Publication Date: 2018-11-06
FOSHAN UNIVERSITY
View PDF8 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since most kernel methods are transductive, t...

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
  • Riemannian manifold maintenance kernel learning method and device based on geodesic distance
  • Riemannian manifold maintenance kernel learning method and device based on geodesic distance
  • Riemannian manifold maintenance kernel learning method and device based on geodesic distance

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] The concept, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and drawings, so as to fully understand the purpose, scheme and effect of the present disclosure. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0048] Such as figure 1 Shown is a flow chart of a Riemannian manifold-preserving kernel learning method based on geodesic distance according to the present disclosure, combined below figure 1 A geodesic distance-based Riemannian manifold-preserving kernel learning method according to an embodiment of the present disclosure will be described.

[0049] The present invention mainly provides a kind of Riemannian manifold preservation kernel learning method based on geodesic distance, and the specific implementation mode of described method is as follow...

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 discloses a Riemannian manifold maintenance kernel learning method and device based on the geodesic distance and aims to solve two problems in the kernel learning method of Riemannian manifold data of digital images, including 1), a measurement maintenance problem, through means of parameterized Mahalanobis distance learning, the distance from the symmetric positive definite matrix on the Riemannian manifold to the Euclidean space is equal to the geodesic distance on the Riemannian manifold, and the optimal solution of the Mahalanobis distance matrix is obtained according to theBregman optimization algorithm; and 2), a fixed kernel method problem. Through kernel learning, according to the Bregman optimization algorithm, the optimal kernel matrix is obtained through kernel learning, the distance in the kernel space is made to be consistent with the geodetic distance, and problems of Riemann manifold measurement maintenance and single kernel limitation are finally solved effectively.

Description

technical field [0001] The present disclosure relates to the field of machine learning and image processing, in particular to a geodesic distance-based Riemannian manifold-preserving kernel learning method and device. Background technique [0002] With the rapid development of multimedia technology and the rapid development of Internet technology, the collection, storage, dissemination and access of digital image information show explosive growth. Digital images are not only flooded on the Internet, but also generate a large amount of image information in civil, commercial, military, medical, biological and other fields, including photos taken in daily life, news pictures, medical pictures, biological pictures, remote sensing pictures, etc. . The above-mentioned situations make image processing more and more complicated. Due to the large quantity and high dimensionality of image data faced by image processing, if traditional methods are used to process images, it will lead...

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
CPCG06F18/2413
Inventor 牛菓王修才顾艳春段志奎陈建文樊耘
Owner FOSHAN UNIVERSITY
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