Conditional random mapping for effective ELM feature representation

Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition task...

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Main Authors: Li, Cheng, Deng, Chenwei, Zhou, Shichao, Zhao, Baojun, Huang, Guang-Bin
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/141688
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1416882020-06-10T03:09:33Z Conditional random mapping for effective ELM feature representation Li, Cheng Deng, Chenwei Zhou, Shichao Zhao, Baojun Huang, Guang-Bin School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Extreme Learning Machine Conditional Random Feature Mapping Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition tasks. This weakness is mainly caused by two critical defects: (1) random feature mappings (RFM) by ad hoc probability distribution is unable to well project various input data into discriminative feature spaces; (2) in the ELM-based hierarchical architectures, features from previous layer are scattered via RFM in the current layer, which leads to abstracting higher level features ineffectively. To address these issues, we aim to take advantage of label information for optimizing random mapping in the ELM, utilizing an efficient label alignment metric to learn a conditional random feature mapping (CRFM) in a supervised manner. Moreover, we proposed a new CRFM-based single-layer ELM (CELM) and then extended CELM to the supervised multi-layer learning architecture (ML-CELM). Extensive experiments on various widely used datasets demonstrate our approach is more effective than original ELM-based and other existing DNN feature representation methods with rapid training/testing speed. The proposed CELM and ML-CELM are able to achieve discriminative and robust feature representation, and have shown superiority in various simulations in terms of generalization and speed. 2020-06-10T03:09:33Z 2020-06-10T03:09:33Z 2018 Journal Article Li, C., Deng, C., Zhou, S., Zhao, B., & Huang, G.-B. (2018). Conditional random mapping for effective ELM feature representation. Cognitive Computation, 10(5), 827-847. doi:10.1007/s12559-018-9557-x 1866-9956 https://hdl.handle.net/10356/141688 10.1007/s12559-018-9557-x 2-s2.0-85046824283 5 10 827 847 en Cognitive Computation © 2018 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Extreme Learning Machine
Conditional Random Feature Mapping
spellingShingle Engineering::Electrical and electronic engineering
Extreme Learning Machine
Conditional Random Feature Mapping
Li, Cheng
Deng, Chenwei
Zhou, Shichao
Zhao, Baojun
Huang, Guang-Bin
Conditional random mapping for effective ELM feature representation
description Extreme learning machine (ELM) has been extensively studied, due to its fast training and good generalization. Unfortunately, the existing ELM-based feature representation methods are uncompetitive with state-of-the-art deep neural networks (DNNs) when conducting some complex visual recognition tasks. This weakness is mainly caused by two critical defects: (1) random feature mappings (RFM) by ad hoc probability distribution is unable to well project various input data into discriminative feature spaces; (2) in the ELM-based hierarchical architectures, features from previous layer are scattered via RFM in the current layer, which leads to abstracting higher level features ineffectively. To address these issues, we aim to take advantage of label information for optimizing random mapping in the ELM, utilizing an efficient label alignment metric to learn a conditional random feature mapping (CRFM) in a supervised manner. Moreover, we proposed a new CRFM-based single-layer ELM (CELM) and then extended CELM to the supervised multi-layer learning architecture (ML-CELM). Extensive experiments on various widely used datasets demonstrate our approach is more effective than original ELM-based and other existing DNN feature representation methods with rapid training/testing speed. The proposed CELM and ML-CELM are able to achieve discriminative and robust feature representation, and have shown superiority in various simulations in terms of generalization and speed.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Cheng
Deng, Chenwei
Zhou, Shichao
Zhao, Baojun
Huang, Guang-Bin
format Article
author Li, Cheng
Deng, Chenwei
Zhou, Shichao
Zhao, Baojun
Huang, Guang-Bin
author_sort Li, Cheng
title Conditional random mapping for effective ELM feature representation
title_short Conditional random mapping for effective ELM feature representation
title_full Conditional random mapping for effective ELM feature representation
title_fullStr Conditional random mapping for effective ELM feature representation
title_full_unstemmed Conditional random mapping for effective ELM feature representation
title_sort conditional random mapping for effective elm feature representation
publishDate 2020
url https://hdl.handle.net/10356/141688
_version_ 1681058034490540032