Multilayer Fisher extreme learning machine for classification

As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the rep...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 9; no. 2; pp. 1975 - 1993
Main Authors: Lai, Jie, Wang, Xiaodan, Xiang, Qian, Wang, Jian, Lei, Lei
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.04.2023
Springer Nature B.V
Springer
Subjects:
Algorithms
Artificial neural networks
Autoencoder
Classification
Complexity
Computational Intelligence
Data Structures and Information Theory
Deep learning
Engineering
Extreme learning machine
Fisher criterion
Machine learning
Multilayers
Original Article
Regularization
Representation learning
Unsupervised learning
Deep learning
Representation learning
Extreme learning machine
Autoencoder
Fisher criterion
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary:As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM).
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-022-00867-7