Multi-view Low-rank Preserving Embedding: A novel method for multi-view representation-Reference-Cited by-同舟云学术

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Multi-view Low-rank Preserving Embedding: A novel method for multi-view representation

Published:2021-03 Issue: Volume:99 Page:104140
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Meng Xiangzhu,Feng Lin,Wang Huibing

Funder

National Natural Science Foundation of China

Liaoning Revitalization Talents Program

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference39 articles.

1. Deep canonical correlation analysis;Andrew,2013

2. Laplacian eigenmaps and spectral techniques for embedding and clustering;Belkin,2002

3. Representation learning: A review and new perspectives;Bengio;IEEE Trans. Pattern Anal. Mach. Intell.,2013

4. Matrix Analysis, Vol. 169;Bhatia,2013

5. Evaluation of gist descriptors for web-scale image search;Douze,2009

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