基于自动加权低秩多视角谱聚类的含噪激光波长分类

李陵江; 陈曙; 杨祖元

doi:10.12052/gdutxb.240058

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基于自动加权低秩多视角谱聚类的含噪激光波长分类

综合研究 | 更新时间：2026-05-27

- 基于自动加权低秩多视角谱聚类的含噪激光波长分类
- Noisy Laser Wavelength Classification Based on Self-weighting Low-rank Multi-view Spectral Clustering
- 广东工业大学学报 2025年42卷第6期页码：116-125
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  李陵江(1990–) ，男，博士研究生，主要研究方向为激光信号处理，E-mail：lagcyp@msn.com
  杨祖元(1982–) ，男，教授，博士，主要研究方向为机器学习、非负矩阵分解和盲信号处理，E-mail：yangzuyuan@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62273106)
- DOI：10.12052/gdutxb.240058
  中图分类号： TP751
- 纸质出版：2025
- 稿件说明：
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李陵江, 陈曙, 杨祖元. 基于自动加权低秩多视角谱聚类的含噪激光波长分类[J]. 广东工业大学学报, 2025, 42(6): 116–125.

Li Lingjiang, Chen Shu, Yang Zuyuan. Noisy Laser Wavelength Classification Based on Self-weighting Low-rank Multi-view Spectral Clustering[J]. 2025, 42(6): 116-125.
李陵江, 陈曙, 杨祖元. 基于自动加权低秩多视角谱聚类的含噪激光波长分类[J]. 广东工业大学学报, 2025, 42(6): 116–125. DOI： 10.12052/gdutxb.240058

Li Lingjiang, Chen Shu, Yang Zuyuan. Noisy Laser Wavelength Classification Based on Self-weighting Low-rank Multi-view Spectral Clustering[J]. 2025, 42(6): 116-125. DOI： 10.12052/gdutxb.240058.

摘要

激光广泛应用于加工和测量等领域，如何在有噪光源干扰下检测激光的波长变化对激光的应用具有重要意义。针对激光波长变化测量的问题，本文提出了一种用于近场光栅衍射成像的多视角谱聚类方法。首先分析对静态近场光栅衍射成像图像进行激光波长变化检测的理论可行性，然后提取衍射图像的多视角特征，最后提出自动调整视图权重的多视角低秩谱聚类方法(Self-weighting Low-rank Multi-view Spectral Clustering

SL-MVSC) 对得到的多视角光栅衍射图像数据进行分类，以实现对激光波长变化的检测。在优化过程中涉及2个自变量，其中一个变量能得到闭式解，另外一个变量的优化能转化为二次规划求解问题。本文在多种噪声干扰环境下进行了实验，实验结果表明，与现有其他方法相比，本文方法在多个聚类指标上都有更好的表现；同时，t-SNE结果表明，本文方法可以将不同簇的样本区分开来，从而证明了在静态近场光栅衍射图像中使用多视角方法对激光波长变化检测的可行性与创新性。

Abstract

Laser is widely used in machining and measurement fields. Detecting the wavelength variation of laser under the interference of noisy light sources is of great significance for its application. In this research

a multi view spectral clustering method used on Fresnel grating diffraction image is proposed to address the issue of laser wavelength variation measurement. Firstly

the theoretical practicability of using static Fresnel grating diffraction image for laser wavelength stability detection is analyzed. Then

diffraction image features from multiple views are extracted. Finally

a self-weighting low-rank multi-view spectral clustering (SL-MVSC) method is proposed to classify the obtained multi-view grating diffraction image data matrix to achieve detection of laser wavelength variation. In the optimization process

there are two variables involved

one of which can obtain a closed form solution

and the optimization of the other variable is transformed into solving a quadratic programming problem. The clustering performance of this method is tested in various noise interference environments. The experiment results show that this method performs better on several clustering performance metrices compared with other four methods. Meanwhile

the t-SNE results indicate that this method can distinguish samples from different clusters. The study proves the practicability and innovation of proposed multi-view method for detecting laser wavelength variation in static Fresnel grating diffraction image.

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references

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