Nonparametric weighted feature extraction for classification

By Bor-Chen Kuo, David A. Landgrebe. IEEE Transactions on Geoscience and Remote Sensing, 2004

449 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/adddcf2731182825d247eb42992a05b4008b794b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Antonio J. Plaza
2. Pedram Ghamisi
3. Jocelyn Chanussot
4. Jun Yu Li
5. Gustau Camps-Valls
6. Javier Plaza
7. Xiuping Jia
8. Jon Atli Benediktsson

In Media Technology

1. Antonio J. Plaza
2. Pedram Ghamisi
3. Jocelyn Chanussot
4. Jon Atli Benediktsson
5. Jun Yu Li
6. Shutao Li
7. Danfeng Hong
8. Javier Plaza

In Statistics and Probability

1. Sonajharia Minz
2. Vipin Kumar
3. Gustau Camps-Valls
4. Xiuping Jia
5. Melba M. Crawford
6. Lorenzo Bruzzone
7. Tatyana V. Bandos Marsheva
8. Javier Plaza

Citations in the corpus

• Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “Several widely used supervised DR methods for HSIs are linear discriminant analysis (LDA) [37], nonparametric weighted feature extraction (NWFE) [38], band selection based on Jeffries–Matsushita (J–M) distance [39], and mutual information [40].”
• Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, unknown): “extraction [16], [34]–[36] techniques, i.”
• Feature Mining for Hyperspectral Image Classification (2013, methodology): “[6] D. A. Landgrebe, ‘‘Multispectral land sensing: Where from, where to?’’”
• A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification (2014, methodology): “Nonparametric weighted feature extraction (NWFE) [47] is a widely used supervised dimensionality reduction method for hyperspectral image data [47], [48].”
• Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis (2009, methodology): “This regularization is sometimes applied in combination with a preliminary feature selection/extraction step in order to increase the aforementioned ratio [31].”
• Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, background): “For evaluating the performance of the proposed SSFC, the classification results of SSFC are compared to other commonly used DR methods and spectral–spatial feature extraction methods, including PCA, LDA, LDE, RLDE, LFDA, NWFE, EMPs [32], and spectral+EMP (SEMP) [33].”
• Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models (2012, background): “the classes’ means is minimized and the interclass variance is maximized [34], [35].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “For instance, linear discriminant analysis (LDA), local Fisher discriminant analysis (LFDA) [101], local discriminant embedding (LDE) [102] and nonparametric weighted feature extraction (NWFE) [103].”
• Feature Selection: A literature Review (2014, methodology): “In [117], pre-processing techniques have been proposed for hyper spectral images in which feature extraction and feature selection have been emphasized as important components in hyper spectral image classification.”