Adaptive View-Aligned and Feature Augmentation Network for Partially View-Aligned Clustering.

As an important task in multi-view clustering, partially view-aligned clustering has attracted increasing attention in recent years. However, previous algorithms have two limitations: (1) they manually calculate the fixed alignment matrix based on Euclidean distance and use the fixed matrix for common feature expression. The manual fixed alignment matrix fails to adequately reflect the similarity of the training data; (2) the process of learning features is isolated from the downstream clustering task, thus learned features are unsuitable for the clustering scenario. In this paper, we propose an adaptive view-aligned and feature augmentation network ( AFAN ) to tackle these two issues. First, we propose an adaptive view-aligned module to calculate the alignment matrix with the self-attention mechanism. The calculated alignment matrix can capture data similarity by jointly learning data features and view alignment. Second, we introduce a self-augmentation strategy to encourage the learned features of the same cluster to be crowded together. Extensive experimental results show that AFAN outperforms state-of-the-art approaches on four benchmark datasets.