Deep network based on stacked orthogonal convex incremental ELM autoencoders
type: Item
instance of: scholarly article
title: Deep network based on stacked orthogonal convex incremental ELM autoencoders
zbMATH Open document ID: 1400.68185
DOI: 10.1155/2016/1649486
author: Chao Wang
author: Jianhui Wang
author: Shusheng Gu
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: Extreme learning machine (ELM) as an emerging technology has recently attracted many researchers' interest due to its fast learning speed and state-of-the-art generalization ability in the implementation. Meanwhile, the incremental extreme learning machine (I-ELM) based on incremental learning algorithm was proposed which outperforms many popular learning algorithms. However, the incremental algorithms with ELM do not recalculate the output weights of all the existing nodes when a new node is added and cannot obtain the least-squares solution of output weight vectors. In this paper, we propose orthogonal convex incremental learning machine (OCI-ELM) with Gram-Schmidt orthogonalization method and Barron's convex optimization learning method to solve the nonconvex optimization problem and least-squares solution problem, and then we give the rigorous proofs in theory. Moreover, in this paper, we propose a deep architecture based on stacked OCI-ELM autoencoders according to stacked generalization philosophy for solving large and complex data problems. The experimental results verified with both UCI datasets and large datasets demonstrate that the deep network based on stacked OCI-ELM autoencoders (DOC-IELM-AEs) outperforms the other methods mentioned in the paper with better performance on regression and classification problems.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 6952819
Wikidata QID: Q59131015
describes a project that uses: ImageNet
describes a project that uses: AlexNet
describes a project that uses: darch
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/1649486
OpenAlex ID: W2463277740
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description: scientific article; zbMATH DE number 6952819