An innovative method for multi-view graph clustering named the graph convolution Transformer network with self-distillation (MvCConvFormer), which applies the Transformer architecture to effectively fuse the structural information of the optimal adjacency matrix and the node representations learned by the pre-training model, thereby mitigating noise interference across multiple views.
This study proposes a model called the Residual compensation graph Convolutional Generalized Extreme Learning Machine Autoencoder (RCGELM-AE), which is built on the Extreme Learning Machine Autoencoder (ELM-AE) and incorporates two key designs, and offers a novel, gradient-descent-free solution for achieving efficient...
Deep neural networks primarily aim to enhance the representation ability of high-level semantic features by deepening the network to reinforce critical features. However, this often leads to the under-utilization or discarding of certain low-level information, resulting in suboptimal performance. Due to differences in...
Rui Zhang, Yue-Long Cheng, Jing-Fan Yang et al.· IEEE Transactions on Image P...· 0 citations
Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on first-order neighbors have long faced several fundamental contradictions: increasing depth leads to over-smoothing, long-range dependencies cause over-compression, fixed neighborhoods rest...
