Adaptive graph constraint multi-view linear discriminant analysis method and system and storage medium

A linear discriminant analysis, multi-view technology, applied in instruments and other directions, can solve problems such as large computational complexity, difficulty in processing large data sets, and common subspaces that are not optimal solutions, to improve performance, avoid waste, and improve stability. and the effect of feature extraction performance

Pending Publication Date: 2022-08-02
XI AN JIAOTONG UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Such a method does not use the label information of the data in the first step of solving the common subspace, which may lead to the learned common subspace is not the optimal solution
[0006] (3) Many existing multi-view feature extraction algorithms are only effective on data sets with a small number of views and a small data size. When the number of views is large or the data size is large, it will be due to too many hyperparameters or other reasons lead to excessive computational complexity, making it difficult to process these large datasets

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
  • Adaptive graph constraint multi-view linear discriminant analysis method and system and storage medium
  • Adaptive graph constraint multi-view linear discriminant analysis method and system and storage medium
  • Adaptive graph constraint multi-view linear discriminant analysis method and system and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] see figure 1 , the adaptive graph-constrained multi-view linear discriminant analysis method of the present invention comprises the following steps:

[0059] S1. Obtain the common low-dimensional representation of multiple views and the projection matrix of each view;

[0060] S2. Use a common low-dimensional representation to adaptively learn a graph that can reflect the internal distribution of the data to constrain the learning process;

[0061] S3, use the method of linear discrimination to learn the optimal projection matrix;

[0062] S4. Perform feature extraction on the multi-view data of the test set through the optimal projection matrix, and input the feature extraction result into the KNN classification system to complete the classification task.

[0063] In an optional implementation manner, step S1 obtains the common low-dimensional representation of the multi-views and the projection matrix of each view by maximizing the typical correlation coefficient be...

Embodiment 2

[0093] An adaptive graph-constrained multi-view linear discriminant analysis method according to an embodiment of the present invention includes the following steps:

[0094] given dataset The number of views of the dataset is M, represents the transformation matrix of the mth view, represents the data of the mth view, represents the common subspace shared by each view, D m Represents the number of dimensions of the mth view, d represents the dimension of S, L S is the graph Laplacian matrix, A is the affinity matrix corresponding to the Laplacian matrix LS.

[0095] It can be proved that linear discriminant analysis and least squares regression are equivalent, that is, expressing LDA as an equivalent LSR form can reduce the redundancy problem in the calculation process, which can be expressed as:

[0096]

[0097] In the multi-view linear discriminant analysis method with adaptive graph constraints, the MAXVAR representation of multi-view canonical correlation ana...

Embodiment 3

[0112] According to the adaptive graph-constrained multi-view linear discriminant analysis method proposed in Embodiment 2, the following steps are performed:

[0113] Step 1: Load the dataset and initialize the matrix S.

[0114] Step 2: Fix b and S, and update the projection matrix W.

[0115] Step 3: Fix W and S, and update the bias term b.

[0116] Step 4: Fix W and b, and update the covariance matrix S.

[0117]Step 5: Calculate U through the covariance matrix S m .

[0118] Step 6: Fix W, b, S, U m The value of , optimizes the affinity matrix A.

[0119] Step 7: Repeat steps 5 to 6 until W, b, S, U m , A converges.

[0120] Step 8: Use W, b, S, U m , A performs feature extraction on the original dataset.

[0121] Step 9: Use the KNN algorithm to classify the extracted features and calculate the classification result.

[0122] Step 10: Calculate the classification accuracy (ACC) according to the classification results.

[0123] Tables 1-6 show the experimenta...

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 an adaptive graph constraint multi-view linear discriminant analysis method and system and a storage medium. The method comprises the following steps: acquiring common low-dimensional representation of multiple views and a projection matrix of each view; a graph capable of reflecting internal distribution of data is adaptively learned through public low-dimensional representation to constrain the learning process; learning an optimal projection matrix by using a linear discrimination method; and performing feature extraction on the test set multi-view data through the optimal projection matrix, and inputting a feature extraction result into a KNN classification system to complete a classification task. According to the method, multi-view canonical correlation analysis and linear discriminant analysis are combined and uniformly expressed, a multi-view learning feature extraction method is more suitable for classification tasks than a single-view feature extraction method, label information of unknown label data is utilized in linear discriminant analysis, common source knowledge induced by graphs is considered at the same time, and the classification efficiency is improved. The distance between expected standard variables is minimized, and fusion extraction and dimension reduction of multi-view data features are realized.

Description

technical field [0001] The invention belongs to the technical field of multi-view data classification, and in particular relates to an adaptive graph-constrained multi-view linear discriminant analysis method, system and storage medium. Background technique [0002] In many practical applications, multi-view learning is usually more robust than single-view learning. A key issue in multi-view learning is how to effectively utilize the information from different feature sets. An efficient method is to fuse the information by acquiring a common subspace for these feature sets, which is usually implemented using feature extraction. Canonical correlation analysis is a classic tool for multi-view learning, which learns a linear projection matrix for each view separately by maximizing the cross-correlation between the two views. Multi-view canonical correlation analysis is an extension of canonical correlation analysis on multi-view data, which can be used to process data with mor...

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): G06V10/764G06V10/774G06V10/80
CPCG06F18/24G06F18/253G06F18/214
Inventor 王飞张家萌郭宇张雪涛赵露婷
Owner XI AN JIAOTONG UNIV
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