High-dimensional Teaching Data Clustering in Sparse Subspaces Based on Information Entropy

Abstract

Due to the large scale and high dimension of teaching data, the using of traditional clustering algorithms has problems such as high computational complexity and low accuracy. Therefore, this paper proposes a weighted block sparse subspace clustering algorithm based on information entropy. The introduction of information entropy weight and block diagonal constraints can obtain the prior probability that two pixels belong to the same category before the simulation experiment, thereby positively intervening that the solutions solved by the model tend to be the optimal approximate solutions of the block diagonal structure. It can enable the model to obtain the performance against noise and outliers, and thereby improving the discriminative ability of the model classification. The experimental results show that the average probability Rand index of the proposed method is 0.86, which is higher than that of other algorithms. The average information change index of the proposed method is 1.55, which is lower than that of other algorithms, proving its strong robustness. On different datasets, the misclassification rates of the design method are 1.2\% and 0.9\% respectively, which proves that its classification accuracy is relatively high. The proposed method has high reliability in processing high-dimensional teaching data. It can play an important role in the field of educational data analysis and provide strong support for intelligent teaching.