Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HS...

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Bibliographic Details
Main Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-Kuen Ling, Huimin Zhao, Stephen Marshall
Format: Article
Language:English
Published: MDPI AG 2017-12-01
Series:Remote Sensing
Subjects:
hyperspectral image (HSI) classification
sparse multinomial logistic regression (SMLR)
extreme sparse multinomial logistic regression (ESMLR)
extended multi-attribute profiles (EMAPs)
linear multiple features learning (MFL)
Lagrange multiplier
Online Access:https://www.mdpi.com/2072-4292/9/12/1255

Internet

https://www.mdpi.com/2072-4292/9/12/1255

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