Hierarchic Neighbors Embedding

TL;DR

Hierarchic Neighbors Embedding (HNE) is a novel manifold learning method that enhances local data connections through hierarchical neighbor combinations, improving performance on sparse and weakly connected data in high-dimensional spaces.

Contribution

This paper introduces HNE, a new manifold learning technique that effectively handles data sparsity by hierarchically combining neighbors, outperforming existing methods on challenging datasets.

Findings

HNE performs well on synthetic and real-world high-dimensional data.

HNE outperforms other manifold learning methods on sparse and weakly connected data.

HNE improves local connection quality through hierarchical neighbor embedding.

Abstract

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.