A Tensor Non-Convex Low Rank And Sparse Constrained Band Selection Scheme For Clustering Of Hyperspectral Paper Data-Reference-Cited by-同舟云学术

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A Tensor Non-Convex Low Rank And Sparse Constrained Band Selection Scheme For Clustering Of Hyperspectral Paper Data

Published:2022-09-13 Issue: Volume: Page:
ISSN:
Container-title:2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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Author:

Francis Jobin¹,Shanthini K S¹,George Sudhish N¹,George Sony²

Affiliation:

1. National Institute of Technology,Dept. of Electronics and Communication Engineering,Calicut,India

2. Norwegian University of Science and Technology,Dept. of Computer Science,Gjøvik,Norway

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9955031/9955024/09955084.pdf?arnumber=9955084

Reference18 articles.

1. Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

2. A Low Tensor-Rank Representation Approach for Clustering of Imaging Data

3. A Comprehensive Tensor Framework for the Clustering of Hyperspectral Paper Data With an Application to Forensic Document Analysis

4. l_{1/2} regularization: A thresholding representation theory and a fast solver;xu;IEEE Transactions on Neural Networks and Learning Systems,2012

5. Parameter Tuning Analysis for Phase Identification Algorithms in Distribution System Model Calibration

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Multi-Attention Network with Multi-Level Spatial-Spectral Feature Fusion Based on Band Selection for Hyperspectral Image Classification;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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