Related papers: Adaptive Locally Linear Embedding

Adaptive Locally Linear Embedding

URL: http://arxiv.org/abs/2504.06829v1
Date: Wed, 09 Apr 2025 12:40:13 GMT
Title: Adaptive Locally Linear Embedding
Authors: Ali Goli, Mahdieh Alizadeh, Hadi Sadoghi Yazdi,
Abstract summary: A novel approach, Adaptive locally linear embedding(ALLE), is introduced to address this limitation.<n> Experimental results demonstrate that ALLE significantly improves the alignment between neighborhoods in the input and feature spaces.<n>This approach advances manifold learning by tailoring distance metrics to the underlying data, providing a robust solution for capturing intricate relationships in high-dimensional datasets.
Score: 10.331256742632835
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Manifold learning techniques, such as Locally linear embedding (LLE), are designed to preserve the local neighborhood structures of high-dimensional data during dimensionality reduction. Traditional LLE employs Euclidean distance to define neighborhoods, which can struggle to capture the intrinsic geometric relationships within complex data. A novel approach, Adaptive locally linear embedding(ALLE), is introduced to address this limitation by incorporating a dynamic, data-driven metric that enhances topological preservation. This method redefines the concept of proximity by focusing on topological neighborhood inclusion rather than fixed distances. By adapting the metric based on the local structure of the data, it achieves superior neighborhood preservation, particularly for datasets with complex geometries and high-dimensional structures. Experimental results demonstrate that ALLE significantly improves the alignment between neighborhoods in the input and feature spaces, resulting in more accurate and topologically faithful embeddings. This approach advances manifold learning by tailoring distance metrics to the underlying data, providing a robust solution for capturing intricate relationships in high-dimensional datasets.

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