<ul><li>Manifold learning techniques, such as Locally linear embedding (LLE), aim to preserve local neighborhood structures of high-dimensional data during dimensionality reduction.</li><li>Adaptive locally linear embedding (ALLE) is introduced as a novel approach to address the limitations of traditional LLE by incorporating a dynamic, data-driven metric for enhanced topological preservation.</li><li>ALLE redefines the concept of proximity by focusing on topological neighborhood inclusion rather than fixed distances, resulting in superior neighborhood preservation and accurate embeddings.</li><li>Experimental results demonstrate that ALLE improves the alignment between neighborhoods in input and feature spaces, providing a robust solution for capturing intricate relationships in high-dimensional datasets.</li></ul>

Adaptive Locally Linear Embedding

Discover more