Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping

Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping

This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports. Given training observations are assumed as a sample from a high-dimensional space with an embedding low-dimensional manifold. An approxim...

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Bibliographic Details
Main Authors: Sheng-Shiung Wu, Sing-Jie Jong, Kai Hu, Jiann-Ming Wu
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Mathematics
Subjects:
unsupervised learning
distance preserving mapping
nonlinear dimensionality reduction mapping
data visualization
topology preservation
data support approximation
Online Access:https://www.mdpi.com/2227-7390/9/9/1017

Internet

https://www.mdpi.com/2227-7390/9/9/1017

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