A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems
An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-10-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/11/10/1284 |
| _version_ | 1811297643533434880 |
|---|---|
| author | Licheng Cui Huawei Zhai Hongfei Lin |
| author_facet | Licheng Cui Huawei Zhai Hongfei Lin |
| author_sort | Licheng Cui |
| collection | DOAJ |
| description | An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM. |
| first_indexed | 2024-04-13T06:07:20Z |
| format | Article |
| id | doaj.art-bf1c3599ea7348d9bf012ced4d0ff100 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-13T06:07:20Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-bf1c3599ea7348d9bf012ced4d0ff1002022-12-22T02:59:12ZengMDPI AGSymmetry2073-89942019-10-011110128410.3390/sym11101284sym11101284A Novel Orthogonal Extreme Learning Machine for Regression and Classification ProblemsLicheng Cui0Huawei Zhai1Hongfei Lin2Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaInformation Science and Technology School, Dalian Maritime University, Dalian 116026, ChinaFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaAn extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.https://www.mdpi.com/2073-8994/11/10/1284extreme learning machineorthogonal constraintorthogonal procrustes problemleast squares regression |
| spellingShingle | Licheng Cui Huawei Zhai Hongfei Lin A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems Symmetry extreme learning machine orthogonal constraint orthogonal procrustes problem least squares regression |
| title | A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems |
| title_full | A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems |
| title_fullStr | A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems |
| title_full_unstemmed | A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems |
| title_short | A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems |
| title_sort | novel orthogonal extreme learning machine for regression and classification problems |
| topic | extreme learning machine orthogonal constraint orthogonal procrustes problem least squares regression |
| url | https://www.mdpi.com/2073-8994/11/10/1284 |
| work_keys_str_mv | AT lichengcui anovelorthogonalextremelearningmachineforregressionandclassificationproblems AT huaweizhai anovelorthogonalextremelearningmachineforregressionandclassificationproblems AT hongfeilin anovelorthogonalextremelearningmachineforregressionandclassificationproblems AT lichengcui novelorthogonalextremelearningmachineforregressionandclassificationproblems AT huaweizhai novelorthogonalextremelearningmachineforregressionandclassificationproblems AT hongfeilin novelorthogonalextremelearningmachineforregressionandclassificationproblems |