1. OP-ELM: Optimally Pruned Extreme Learning Machine
- Author
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Amaury Lendasse, Christian Jutten, Patrick Bas, Olli Simula, Yoan Miche, Antti Sorjamaa, Laboratory of Computer and Information Science (CIS), TKK Helsinki University of Technology (TKK), GIPSA - Communication, Signal et Sécurité (GIPSA-C2S), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Laboratory of Computer and Information Science ( CIS ), Helsinki University of Technology ( TKK ), C2S ( GIPSA-C2S ), Département Images et Signal ( GIPSA-DIS ), Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Grenoble Images Parole Signal Automatique ( GIPSA-lab ), Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Pierre Mendès France - Grenoble 2 ( UPMF ) -Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ), and SIGMAPHY ( GIPSA-SIGMAPHY )
- Subjects
0209 industrial biotechnology ,Time Factors ,Computer science ,Normal Distribution ,[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing ,02 engineering and technology ,computer.software_genre ,020901 industrial engineering & automation ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,0202 electrical engineering, electronic engineering, information engineering ,ComputingMilieux_MISCELLANEOUS ,Extreme learning machine ,Neurons ,Artificial neural network ,Signal Processing, Computer-Assisted ,Regression analysis ,General Medicine ,Regression, Psychology ,Computer Science Applications ,extreme learning machine (ELM) ,classification ,Multilayer perceptron ,symbols ,Feedforward neural network ,regression ,020201 artificial intelligence & image processing ,least angle regression (LARS) ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Algorithms ,variable selection ,[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing ,Computer Networks and Communications ,Feature selection ,Machine learning ,Online Systems ,symbols.namesake ,Artificial Intelligence ,Robustness (computer science) ,Humans ,Computer Simulation ,optimally pruned extreme learning machine (OP-ELM) ,Gaussian process ,ta113 ,business.industry ,Pattern recognition ,Support vector machine ,Nonlinear Dynamics ,Perception ,Artificial intelligence ,business ,computer ,Software - Abstract
International audience; In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square error) are compared to the original ELM and to three other widely used methodologies: multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate, the proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM. Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online.
- Published
- 2010