The manuscript of this work has been submitted to International Journal of Machine Learning and Cybernetics in Nov. 2024

All the experiments were conducted with 8 RAM, 64-bit Windows 10 and Inter Core i5 processor.

All the codes are implemented with MATLAB 2018a.

1.demo_once.m #The main functions.

2.L2_distance_1.m #Compute squared Euclidean distance

3.GSCEFW.m #The function of our proposed GSCEFW algorithm.

4.ConstructW.m #The function of the graph construction algorithm.

5.EuDist2.m #EuDist2 compute the Euclidean distance matrix.

6.eig1.m # compute the eigenvalue and eigenvector.

7.EProjSimplex_new.m #The function used to solve for S.

8.L2_distance_1.m #Compute squared Euclidean distance.

9.ClusteringMeasure.m #Compute the clustering performance of the algorithm based on K-means.

10./Ncut_9/ # The Normalized cut code can be obtained readily by searching it on Github.

For any problem about this dataset or codes, please contact Dr. Jiang ([email protected]).