关键词: 关键词: 不完备多视图聚类, K最近邻, 注意力机制, 皮尔逊相关系数, k均值聚类算法

Abstract: Abstract: In order to solve the problems of uncertainty in completing missing view data, lack of robustness of embedding learning and low model generalization in traditional deep incomplete multi-view clustering algorithms, an Incomplete Multi-View Clustering algorithm based on Attention Mechanism (IMVCAM) was proposed. First, K-Nearest Neighbors (KNN) was used to complete the missing data in the view, making the training data complementary. Then, after passing the linear encoding layer, the obtained embedding was passed through the attention layer to improve the quality of the embedding. Finally, the embedding obtained from the training of each view was clustered using the k-means clustering algorithm (k-means), and the weights of the views were determined by the Pearson correlation coefficient. The experiments were conducted on five classic datasets, and the best results were achieved on the Fashion dataset. Experimental results on the Fashion dataset showed that compared with the suboptimal DSIMVC (Deep Safe Incomplete Multi-View Clustering), the proposed algorithm IMVCAM improved the clustering accuracy by 2.85 and 4.35 percentage points when the data missing rate was 0.1 and 0.3 respectively. In addition, on the Caltech101-20 dataset, the clustering accuracy increased by 7.68 and 3.48 percentage points compared to the suboptimal IMVCSAF (Incomplete Multi-View Clustering algorithm based on Self-Attention Fusion) when the missing rate was 0.1 and 0.3.

Key words: Keywords: Incomplete multi-view clustering, Attention mechanism, K-Nearest Neighbors (KNN), k-means clustering algorithm (k-means), Pearson correlation coefficient