Hierarchic Neighbors Embedding
This addresses data sparsity in manifold learning, an incremental improvement for nonlinear data analysis applications.
The paper tackles the problem of data sparsity in manifold learning by proposing Hierarchic Neighbors Embedding (HNE), which enhances local connections through hierarchical neighbor combinations. Experimental results show HNE performs well on synthetic and real-world data, with better performance on sparse samples and weak-connected manifolds compared to other methods.
Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE.