A Dimensionality Reduction and Reconstruction Method for Data with Multiple Connected Components-Reference-Cited by-同舟云学术

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A Dimensionality Reduction and Reconstruction Method for Data with Multiple Connected Components

Published:2022-07-08 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 5th International Conference on Big Data and Artificial Intelligence (BDAI)
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Author:

Yao Yuqin¹,Gao Yang²,Long Zhiguo²,Meng Hua¹,Sioutis Michael³

Affiliation:

1. School of Mathematics Southwest Jiaotong University,Chengdu,China

2. School of Computing & Artificial Intelligence Southwest Jiaotong University,Chengdu,China

3. Faculty of Information Systems and Applied Computer Sciences, University of Bamberg,Bavaria,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9862526/9862610/09862787.pdf?arnumber=9862787

Reference25 articles.

1. Laplacian eigenmaps and spectral techniques for embedding and clustering;belkin;Advances in neural information processing systems,2001

2. Nonlinear Dimensionality Reduction by Locally Linear Embedding

3. Locality preserving projections;he;Advances in neural information processing systems,2003

4. Conformal Metric Optimization on Surface (CMOS) for Deformation and Mapping in Laplace-Beltrami Embedding Space

5. LPP solution schemes for use with face recognition

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