SpectralMAP: Approximating Data Manifold With Spectral Decomposition

SpectralMAP: Approximating Data Manifold With Spectral Decomposition

Dimensionality reduction is widely used to visualize complex high-dimensional data. This study presents a novel method for effective data visualization. Previous methods depend on local distance measurements for data manifold approximation. This leads to unreliable results when a data manifold local...

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Bibliographic Details
Main Authors: Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Data visualization
dimensionality reduction
spectral-based filtering
Online Access:https://ieeexplore.ieee.org/document/10070750/

Internet

https://ieeexplore.ieee.org/document/10070750/

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