Multi-view learning has attracted wide attention because of the ability to integrate information from different views. For the issue of multi-view data fusion, a co-consensus multi-view spectral clustering method is proposed. The method adds two consensus constraints in the model of spectral clustering to utilize the feature relationship and the similarity relationship of different views’ spectral embedding matrices which enhances the consistency of multi views. Simultaneously, this method obtains closed solution of the consensus variables in the optimization process, which further improves the clustering performance. The experiment tests the convergence, parameter sensitivity and clustering performance of the proposed method in three real-world datasets. The experiment results show that this method has the best performance in multiple performance metrics compared with the existing methods, and the maximum improvement in clustering accuracy is more than 10%. The experiment proves the co-consensus method effectively improves the performance of multi-view spectral clustering algorithm.

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.