Knowledge graph embedding (KGE) is a task to transform the symbolic entities and relations in Knowledge Graphs(KGs) into low-dimensional vectors, which facilitates the use of KGs in downstream applications. How
交叉注意力融合模块 交叉注意力融合机制具有全局学习能力和良好的并行性,可以在抑制无用噪声的同时,进一步突出融合表示中的关键信息。 交叉注意力融合机制定义如下: 我这里其实不太理解,公式5应该是一个自注意力机制的公式,QKV都是Y。而Y中又包含手动指定的参数γ,那注意力机制的意义何在?如果有理解的小伙伴欢迎在...
Learning robust affinity graph representation for multi-view clustering Inform. Sci. (2021) ChangS. et al. Multi-view clustering via deep concept factorization Knowl.-Based Syst. (2021) LiH. et al. Robust energy preserving embedding for multi-view subspace clustering Knowl.-Based Syst. (2020) ...
Notably, GCN- V was trained on the entire graph rather than subgraphs, resulting in less learning bias. However, only a one-layer GCN was deployed to reduce the computational cost, limit- ing the representation power of the GCN. Guo et al. designed a density-aware feature embedding network...
Fast semi-supervised clustering with enhanced spectral embedding. Pattern Recognit. 2012, 45, 4358–4369. [Google Scholar] [CrossRef] Kim, T.H.; Lee, K.M.; Lee, S.U. Learning full pairwise affinities for spectral segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1690–...
Fast semi-supervised clustering with enhanced spectral embedding. Pattern Recognit. 2012, 45, 4358–4369. [Google Scholar] [CrossRef] Kim, T.H.; Lee, K.M.; Lee, S.U. Learning full pairwise affinities for spectral segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1690–...