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

Missing multi-view data classification method and system

A classification method and multi-view technology, applied in the field of classification methods and systems lacking multi-view data, can solve problems such as inability to fully mine data relationships, failure to apply, and difficulty in modeling

Active Publication Date: 2019-12-06
TIANJIN UNIV
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These different types of data contain complementary information that is effective for disease diagnosis. However, it is difficult to integrate multiple types of data and make full use of them. In addition, the lack of partial view data makes modeling more difficult.
[0003] Although the field of multi-view learning has developed rapidly in recent years, it is still limited by the effective modeling of complex relationships, and it is difficult for existing technologies to effectively solve the situation of missing views.
In dealing with the problem of missing views, some technologies discard missing data and only keep complete data, which will lose a lot of data information, especially when the sample size is scarce; some technologies are grouped according to the missing data, and each group is trained independently. It will not be able to fully mine the relationship between the data, and it will also lead to complex grouping when the missing situation is diverse
This leads to the fact that the existing classification methods for missing multi-view data cannot balance the consistency relationship and information complementarity between multi-view data, resulting in low classification accuracy.

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
  • Missing multi-view data classification method and system
  • Missing multi-view data classification method and system
  • Missing multi-view data classification method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0050] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0051] The classification method for missing multi-view data provided by the present invention includes a training process and a testing process. The overall idea of ​​the method is:

[0052] training process

...

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 missing multi-view data classification method and system. The method comprises the following steps: reconstructing a first implicit space by missing multi-view training sample data, and reconstructing a second implicit space by missing multi-view sample data to be tested; training sample data and a reconstruction loss function by the first implicit space and the missing multi-view; training a multi-view multi-path neural network model, inputting the first implicit space and the real class label into the trained model, and adjusting the first implicit space by taking the total loss function as a target function until the reconstruction loss function and the total loss function are converged to obtain a trained model and a first complete implicit space; inputting the second implicit space into the trained model, and adjusting the second implicit space by taking the reconstruction loss function as a target function to obtain a second complete implicit space; andclassifying the missing multi-view sample to be tested according to the first complete hidden space and the second complete hidden space. According to the invention, the accuracy of classifying missing multi-view data can be improved.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to a classification method and system for missing multi-view data. Background technique [0002] Multi-view data is very common in life, for example, magnetic resonance imaging and computed tomography in the medical field. These different types of data contain complementary information that is effective for disease diagnosis. However, it is difficult to integrate multiple types of data and make full use of them. In addition, the lack of partial view data makes modeling more difficult. [0003] Although the field of multi-view learning has developed rapidly in recent years, it is still limited by the effective modeling of complex relationships, and it is difficult for existing technologies to effectively solve the situation of missing views. In dealing with the problem of missing views, some technologies discard missing data and only keep complete data, which will lose ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/24G06F18/214
Inventor 张长青崔雅洁韩宗博
Owner TIANJIN 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