14.2.21 Extreme Learning Machine, ELM

Zhu, Q.Y.[Qin-Yu], Qin, A.K., Suganthan, P.N., Huang, G.B.[Guang-Bin],
Evolutionary extreme learning machine,
PR(38), No. 10, October 2005, pp. 1759-1763.
Elsevier DOI 0508
BibRef

Chen, C.[Chen], Li, W.[Wei], Su, H.J.[Hong-Jun], Liu, K.[Kui],
Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine,
RS(6), No. 6, 2014, pp. 5795-5814.
DOI Link 1407
BibRef

Li, W.[Wei], Chen, C.[Chen], Su, H.J.[Hong-Jun], Du, Q.[Qian],
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification,
GeoRS(53), No. 7, July 2015, pp. 3681-3693.
IEEE DOI 1503
Educational institutions BibRef

Li, J.J.[Jiao-Jiao], Xi, B.B.[Bo-Bo], Du, Q.[Qian], Song, R.[Rui], Li, Y.S.[Yun-Song], Ren, G.B.[Guang-Bo],
Deep Kernel Extreme-Learning Machine for the Spectral-Spatial Classification of Hyperspectral Imagery,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Riccardi, A., Fernandez-Navarro, F., Carloni, S.,
Cost-Sensitive AdaBoost Algorithm for Ordinal Regression Based on Extreme Learning Machine,
Cyber(44), No. 10, October 2014, pp. 1898-1909.
IEEE DOI 1410
learning (artificial intelligence) BibRef

Lopes, N.[Noel], Ribeiro, B.[Bernardete],
Towards adaptive learning with improved convergence of deep belief networks on graphics processing units,
PR(47), No. 1, 2014, pp. 114-127.
Elsevier DOI 1310
BibRef
And: A2, A2:
Extreme Learning Classifier with Deep Concepts,
CIARP13(I:182-189).
Springer DOI 1311
Deep learning BibRef

Shen, Y.[Yu], Zhu, S.J.[Si-Jie], Chen, C.[Chen], Du, Q.[Qian], Xiao, L.[Liang], Chen, J.Y.[Jian-Yu], Pan, D.[Delu],
Efficient Deep Learning of Nonlocal Features for Hyperspectral Image Classification,
GeoRS(59), No. 7, July 2021, pp. 6029-6043.
IEEE DOI 2106
Feature extraction, Data mining, Kernel, Machine learning, Support vector machines, Neural networks, Computational modeling, nonlocal module BibRef

Chen, Z.F.[Zhi-Fei], Hao, Y.[Yang], Liu, Q.C.[Qi-Chao], Liu, Y.[Yuyong], Zhu, M.Y.[Ming-Yang], Xiao, L.[Liang],
Deep Learning for Hyperspectral Image Classification: A Critical Evaluation via Mutation Testing,
RS(16), No. 24, 2024, pp. 4695.
DOI Link 2501
BibRef

Li, Z.K.[Zhao-Kui], Liu, M.[Ming], Chen, Y.S.[Yu-Shi], Xu, Y.M.[Yi-Min], Li, W.[Wei], Du, Q.[Qian],
Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification,
GeoRS(60), 2022, pp. 1-18.
IEEE DOI 2112
Training, Task analysis, Feature extraction, Data models, Deep learning, Hyperspectral imaging, Adaptation models, meta-learning BibRef

Li, J.J.[Jiao-Jiao], Zhang, Z.Y.[Zhi-Yuan], Song, R.[Rui], Li, Y.S.[Yun-Song], Du, Q.[Qian],
SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification,
IP(33), 2024, pp. 840-855.
IEEE DOI 2402
Task analysis, Data models, Correlation, Transformers, Feature extraction, Convolution, Indexes, Cross-domain, HSI classification BibRef

Li, W.[Wei], Wu, G.D.[Guo-Dong], Zhang, F.[Fan], Du, Q.[Qian],
Hyperspectral Image Classification Using Deep Pixel-Pair Features,
GeoRS(55), No. 2, February 2017, pp. 844-853.
IEEE DOI 1702
hyperspectral imaging BibRef

Ye, Y.B.[Yi-Bin], Qin, Y.[Yang],
QR factorization based Incremental Extreme Learning Machine with growth of hidden nodes,
PRL(65), No. 1, 2015, pp. 177-183.
Elsevier DOI 1511
Extreme Learning Machine(ELM) BibRef

Xu, X.Z.[Xin-Zheng], Wang, G.Y.[Guan-Ying], Ding, S.F.[Shi-Fei], Jiang, X.Y.[Xiang-Ying], Zhao, Z.P.[Zuo-Peng],
A new method for constructing granular neural networks based on rule extraction and extreme learning machine,
PRL(67, Part 2), No. 1, 2015, pp. 138-144.
Elsevier DOI 1512
Granular neural networks BibRef

Shen, Q.[Qing], Ban, X.J.[Xiao-Juan], Liu, R.[Ruoyi], Wang, Y.[Yu],
Decay-weighted extreme learning machine for balance and optimization learning,
MVA(28), No. 7, October 2017, pp. 743-753.
WWW Link. 1710
BibRef

Zhang, Y.S.[Yong-Shan], Wu, J.[Jia], Zhou, C.[Chuan], Cai, Z.H.[Zhi-Hua],
Instance cloned extreme learning machine,
PR(68), No. 1, 2017, pp. 52-65.
Elsevier DOI 1704
Extreme Learning Machine BibRef

Cvetkovic, S.[Stevica], Stojanovic, M.B.[Miloš B.], Nikolic, S.V.[Saša V.],
Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images,
SP:IC(39, Part A), No. 1, 2015, pp. 111-120.
Elsevier DOI 1512
Scene classification BibRef

Quispe, A.M.[Arturo Mendoza], Petitjean, C.[Caroline], Heutte, L.[Laurent],
Extreme learning machine for out-of-sample extension in Laplacian eigenmaps,
PRL(74), No. 1, 2016, pp. 68-73.
Elsevier DOI 1604
Dimensionality reduction BibRef

Kasun, L.L.C., Yang, Y., Huang, G.B., Zhang, Z.,
Dimension Reduction With Extreme Learning Machine,
IP(25), No. 8, August 2016, pp. 3906-3918.
IEEE DOI 1608
Additives BibRef

Zhang, L., Zhang, D.,
Robust Visual Knowledge Transfer via Extreme Learning Machine-Based Domain Adaptation,
IP(25), No. 10, October 2016, pp. 4959-4973.
IEEE DOI 1610
image classification BibRef

Zhang, Y.S.[Yong-Shan], Wu, J.[Jia], Cai, Z.H.[Zhi-Hua], Zhang, P.[Peng], Chen, L.[Ling],
Memetic Extreme Learning Machine,
PR(58), No. 1, 2016, pp. 135-148.
Elsevier DOI 1606
Extreme Learning Machine BibRef

Chen, B.D.[Ba-Dong], Wang, X.[Xin], Lu, N.[Na], Wang, S.Y.[Shi-Yuan], Cao, J.W.[Jiu-Wen], Qin, J.[Jing],
Mixture correntropy for robust learning,
PR(79), 2018, pp. 318-327.
Elsevier DOI 1804
Correntropy, Mixture correntropy, Robust learning, Extreme learning machine, Kernel adaptive filtering BibRef

Haut, J.M.[Juan Mario], Paoletti, M.E.[Mercedes Eugenia], Plaza, J.[Javier], Plaza, A.[Antonio],
Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines,
RealTimeIP(14), No. 3, October 2018, pp. 439-462.
Springer DOI 1811
BibRef

Li, R.[Rui], Wang, X.D.[Xiao-Dan], Lei, L.[Lei], Wu, C.M.[Chong-Ming],
Representation learning by hierarchical ELM auto-encoder with double random hidden layers,
IET-CV(13), No. 4, June 2019, pp. 411-419.
DOI Link 1906
ELM: Extreme Learning Machine. BibRef

Chen, L.J.[Liang-Jun], Honeine, P.[Paul], Hua, Q.[Qu], Zhao, J.H.[Ji-Hong], Xia, S.[Sun],
Correntropy-based robust multilayer extreme learning machines,
PR(84), 2018, pp. 357-370.
Elsevier DOI 1809
Deep learning, Extreme learning machine, Correntropy, Unsupervised feature learning, Computer aided cancer diagnosis BibRef

Cai, Y.M.[Yao-Ming], Liu, X.B.[Xiao-Bo], Zhang, Y.S.[Yong-Shan], Cai, Z.H.[Zhi-Hua],
Hierarchical ensemble of Extreme Learning Machine,
PRL(116), 2018, pp. 101-106.
Elsevier DOI 1812
Extreme Learning Machine, Ensemble learning, Representation learning BibRef

Pradhan, M.K.[Monoj K.], Minz, S.[Sonajharia], Shrivastava, V.K.[Vimal K.],
Fast active learning for hyperspectral image classification using extreme learning machine,
IET-IPR(13), No. 4, March 2019, pp. 549-555.
DOI Link 1903
BibRef

Cao, F., Yang, Z., Ren, J., Chen, W., Han, G., Shen, Y.,
Local Block Multilayer Sparse Extreme Learning Machine for Effective Feature Extraction and Classification of Hyperspectral Images,
GeoRS(57), No. 8, August 2019, pp. 5580-5594.
IEEE DOI 1908
feature extraction, feedforward neural nets, geophysical image processing, hyperspectral imaging, loopy belief propagation (LBP) BibRef

Zhang, W.Y.[Wen-Yu], Zhang, Z.J.[Zhen-Jiang], Wang, L.[Lifu], Chao, H.C.[Han-Chieh], Zhou, Z.B.[Zhang-Bing],
Extreme learning machines with expectation kernels,
PR(96), 2019, pp. 106960.
Elsevier DOI 1909
Bounded random feature mapping, Expectation kernel, Extreme learning machine, Kernel learning, Random sampling BibRef

Zhang, Y.S.[Yong-Shan], Jiang, X.W.[Xin-Wei], Wang, X.X.[Xin-Xin], Cai, Z.H.[Zhi-Hua],
Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Pashaei, A.[Ali], Ghatee, M.[Mehdi], Sajedi, H.[Hedieh],
Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images,
RealTimeIP(17), No. 4, August 2020, pp. 1051-1066.
WWW Link. 2007
BibRef

Wang, Z.X.[Zhao-Xi], Chen, S.Y.[Sheng-Yong], Guo, R.W.[Rong-Wei], Li, B.[Bin], Feng, Y.B.[Yang-Bo],
Extreme learning machine with feature mapping of kernel function,
IET-IPR(14), No. 11, September 2020, pp. 2495-2502.
DOI Link 2009
BibRef

Raghuwanshi, B.S.[Bhagat Singh], Shukla, S.[Sanyam],
Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning,
PR(119), 2021, pp. 108069.
Elsevier DOI 2106
Extreme learning machine, Minimum variance-embedded class-specific kernelized extreme learning machine, Classification BibRef

Yu, X.[Xumin], Feng, Y.[Yan], Gao, Y.L.[Yan-Long], Jia, Y.B.[Ying-Biao], Mei, S.H.[Shao-Hui],
Dual-Weighted Kernel Extreme Learning Machine for Hyperspectral Imagery Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Wu, C.[Chao], Li, Y.Q.[Ya-Qian], Zhang, Y.[Yaru], Liu, B.[Bin],
Double constrained bag of words for human action recognition,
SP:IC(98), 2021, pp. 116399.
Elsevier DOI 2109
Human action recognition, Bag of words (boW), Feature encoding, Extreme learning machine (ELM) BibRef

He, Y.L.[Yu-Lin], Xu, S.S.[Sheng-Sheng], Huang, J.Z.[Joshua Zhexue],
Creating synthetic minority class samples based on autoencoder extreme learning machine,
PR(121), 2022, pp. 108191.
Elsevier DOI 2109
Imbalanced classification, Minority class, Majority class, Synthetic samples creation, SMOTE BibRef

Han, T.[Te], Tang, Y.Q.[Yu-Qi], Yang, X.[Xin], Lin, Z.F.[Ze-Feng], Zou, B.[Bin], Feng, H.H.[Hui-Hui],
Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM),
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Wang, S.S.[Shan-Shan], Liu, Z.[Zheng], Xie, R.[Rong], Ran, L.[Lei],
Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, H.S.[Hui-Sheng], Wang, Z.Q.[Zai-Qiang], Chen, D.[Dehao], Zhu, S.[Shuai], Xu, D.[Dongpo],
Quaternion Extreme Learning Machine Based on Real Augmented Representation,
SPLetters(30), 2023, pp. 175-179.
IEEE DOI 2303
Quaternions, Signal processing algorithms, Computational modeling, Training, Mathematical models, widely linear estimation BibRef

Yang, S.H.[Shu-Han], Tian, S.[Shufang],
Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine,
RS(16), No. 19, 2024, pp. 3646.
DOI Link 2410
BibRef

Zhang, H.R.[Hong-Rui], Lv, H.F.[Hong-Fei], Wang, M.K.[Meng-Ke], Wang, L.Y.[Lu-Yao], Xu, J.H.[Jin-Huan], Wang, F.G.[Feng-Gui], Li, X.D.[Xiang-Dong],
Unsupervised hyperspectral images classification using hypergraph convolutional extreme learning machines,
IET-IPR(18), No. 9, 2024, pp. 2389-2399.
DOI Link 2407
hyperspectral imaging, image classification, unsupervised learning BibRef

Alcacer, A.[Aleix], Epifanio, I.[Irene], Gual-Arnau, X.[Ximo],
Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes,
PAMI(46), No. 12, December 2024, pp. 8228-8239.
IEEE DOI 2411
Prototypes, Gene expression, Elbow, Sports, Sociology, Proposals, Data analysis, Archetype analysis, biclustering, prototype, unsupervised learning BibRef

Verma, Y.[Yashaswi],
Surprising Effectiveness of Random Feature Embeddings in eXtreme Classification,
ICPR22(1836-1842)
IEEE DOI 2212
Training, Vocabulary, Codes, XML, Benchmark testing, Reproducibility of results BibRef

Rodriguez, P.[Paul],
Random Generated Dictionaries for Convolutional Sparse Coding: An ELM Interpretation for Simple CSC Applications,
ICIP22(126-130)
IEEE DOI 2211
Convolutional codes, Training, Dictionaries, Image coding, Extreme learning machines, Noise reduction, Superresolution BibRef

Mittal, A.[Anshul], Dahiya, K.[Kunal], Malani, S.[Shreya], Ramaswamy, J.[Janani], Kuruvilla, S.[Seba], Ajmera, J.[Jitendra], Chang, K.H.[Keng-Hao], Agarwal, S.[Sumeet], Kar, P.[Purushottam], Varma, M.[Manik],
Multi-modal Extreme Classification,
CVPR22(12383-12392)
IEEE DOI 2210
Training, Representation learning, Bridges, Visualization, Search engines, BibRef

Xu, J.H.[Jin-Huan], Liu, P.F.[Peng-Fei], Sun, L.[Le], Xiao, L.[Liang],
Discriminative Pixel-Pairwise Constraint-Guided Extreme Learning Machine for Semi-Supervised Hyperspectral Image Classification,
ICIP18(1518-1522)
IEEE DOI 1809
Hyperspectral imaging, Support vector machines, Manifolds, Laplace equations, Computational modeling, Linear programming, extreme learning machine (ELM) BibRef

Li, Z.Z.[Zhuang-Zi], Zhu, X.B.[Xiao-Bin], Wang, L.[Lei], Guo, P.Y.[Pei-Yu],
Image Classification Using Convolutional Neural Networks and Kernel Extreme Learning Machines,
ICIP18(3009-3013)
IEEE DOI 1809
Feature extraction, Training, Kernel, Task analysis, Convolutional neural networks, Support vector machines, BibRef

Cui, D.S.[Dong-Shun], Zhang, G.H.[Guang-Hao], Han, W.[Wei],
Compact Feature Representation for Image Classification Using ELMs,
CEFR-LCV17(1015-1022)
IEEE DOI 1802
Extreme Learning Machine. Shallow network. Convolution, Feature extraction, Noise measurement, Principal component analysis, Support vector machines BibRef

Lekamalage, C.K.L., Song, K., Huang, G.B., Cui, D., Liang, K.,
Multi layer multi objective extreme learning machine,
ICIP17(1297-1301)
IEEE DOI 1803
Linear programming, Multi-layer neural network, Neurons, Optical character recognition software, Task analysis, Testing, image classification BibRef

Cui, D., Huang, G.B., Kasun, L.L.C., Zhang, G., Han, W.,
Elmnet: Feature learning using extreme learning machines,
ICIP17(1857-1861)
IEEE DOI 1803
Convolution, Histograms, Noise measurement, Principal component analysis, Task analysis, Training, ELM-AE, Feature Learning BibRef

Guo, L., Liew, A.W.C.[Alan Wee-Chung],
An Improved Extreme Learning Machine with Parallelized Feature Mapping Structures,
DICTA16(1-5)
IEEE DOI 1701
Diabetes BibRef

Tang, J.X.[Jie-Xiong], Deng, C.W.[Chen-Wei], Huang, G.B.[Guang-Bin], Hou, J.H.[Jun-Hui],
A fast learning algorithm for multi-layer extreme learning machine,
ICIP14(175-178)
IEEE DOI 1502
Accuracy BibRef

Liu, Y.[Yu], Xu, X.J.[Xiu-Juan], Wang, C.Y.[Chun-Yu],
Simple Ensemble of Extreme Learning Machine,
CISP09(1-5).
IEEE DOI 0910
BibRef

Cao, L.L.[Le-Le], Huang, W.B.[Wen-Bing], Sun, F.C.[Fu-Chun],
Optimization-Based Extreme Learning Machine with Multi-kernel Learning Approach for Classification,
ICPR14(3564-3569)
IEEE DOI 1412
Kernel BibRef

Shao, Z.F.[Zhi-Fei], Er, M.J.[Meng Joo], Huang, G.B.[Guang-Bin],
Receding Horizon Cache and Extreme Learning Machine based Reinforcement Learning,
ICARCV12(1591-1596).
IEEE DOI 1304
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Robust Techniques, Robust Classification .