Latent Feature Representation-Based Low Rank Subspace Clustering for Hyperspectral Band Selection

Xiaodi Shang; Xin Zhao; Yujie Guo; Xudong Sun

doi:10.1007/978-981-97-8493-6_29

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Latent Feature Representation-Based Low Rank Subspace Clustering for Hyperspectral Band Selection

Xiaodi Shang Xin Zhao Yujie Guo Xudong Sun

Year: 2024 Lecture notes in computer science Pages: 416-429 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-97-8493-6_29

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Keywords:

Computer science Hyperspectral imaging Cluster analysis Pattern recognition (psychology) Artificial intelligence Representation (politics) Feature selection Subspace topology Rank (graph theory) Selection (genetic algorithm) Data mining Mathematics

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Remote-Sensing Image Classification

Physical Sciences → Engineering → Media Technology

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Chemical Sensor Technologies

Physical Sciences → Engineering → Biomedical Engineering

Latent Feature Representation-Based Low Rank Subspace Clustering for Hyperspectral Band Selection

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