论文标题
当地的邻居传播嵌入
Local Neighbor Propagation Embedding
论文作者
论文摘要
多种学习在降低非线性维度的领域中起着至关重要的作用，其思想也适用于其他相关方法。基于图形的方法，例如图形卷积网络（GCN），尽管它们属于不同的领域，但仍与流形学习共同。受GCN的启发，我们将邻居传播引入LLE，并提出当地的邻居传播嵌入（LNPE）。与LLE相比，随着线性计算复杂性的增加，LNPE通过将$ 1 $ -HOP的邻居扩展到$ N $ -HOP邻居，从而增强了社区之间的本地连接和交互。实验结果表明，LNPE具有更好的拓扑和几何特性，可以获得更忠实，稳健的嵌入。
Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Convolutional Networks (GCN) show ideas in common with manifold learning, although they belong to different fields. Inspired by GCN, we introduce neighbor propagation into LLE and propose Local Neighbor Propagation Embedding (LNPE). With linear computational complexity increase compared with LLE, LNPE enhances the local connections and interactions between neighborhoods by extending $1$-hop neighbors into $n$-hop neighbors. The experimental results show that LNPE could obtain more faithful and robust embeddings with better topological and geometrical properties.
