Extreme learning machine with autoencoding receptive fields for image classification

Wu, Chao; Li, Yaqian; Zhao, Zhimin; Liu, Bin

doi:10.1007/s00521-019-04303-9

Cited by 8 publications

(16 citation statements)

References 21 publications

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“…en, all the related parameters of these hidden layers can be obtained after the (M-2)-iteration operation. To make the final actual hidden layer output close to the expected hidden layer output, in the training stage, the MELM designs a novel parameter-setting step for the newly added hidden layer, as described in (8), which is the key step to guarantee the stability and feasibility of the MELM model.…”

Section: Multilayered Elm (Melm)mentioning

confidence: 99%

“…In the ELM algorithm, the input weights and hidden biases are randomly generated from any continuous probability distribution, and then, the output weights can be solved using the generalized Moore-Penrose inverse. Compared with the BP neural network, this algorithm has a good performance network in regression [4][5][6], classification [7][8][9], feature learning [10][11][12], and cluster tasks [13][14][15]. Different from conventional gradient-based neural network learning algorithms, which are sensitive to the combination of parameters and easy to trap in local optimum, ELM not only has a faster training speed but also has a smaller training error.…”

Section: Introductionmentioning

confidence: 99%

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Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Liu

et al. 2021

Mathematical Problems in Engineering

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Extreme learning machine is originally proposed for the learning of the single hidden layer feedforward neural network to overcome the challenges faced by the backpropagation (BP) learning algorithm and its variants. Recent studies show that ELM can be extended to the multilayered feedforward neural network in which the hidden node could be a subnetwork of nodes or a combination of other hidden nodes. Although the ELM algorithm with multiple hidden layers shows stronger nonlinear expression ability and stability in both theoretical and experimental results than the ELM algorithm with the single hidden layer, with the deepening of the network structure, the problem of parameter optimization is also highlighted, which usually requires more time for model selection and increases the computational complexity. This paper uses Cholesky factorization strategy and Givens rotation transformation to choose the hidden nodes of MELM and obtains the number of nodes more suitable for the network. First, the initial network has a large number of hidden nodes and then uses the idea of ridge regression to prune the nodes. Finally, a complete neural network can be obtained. Therefore, the ELM algorithm eliminates the need to manually set nodes and achieves complete automation. By using information from the previous generation’s connection weight matrix, it can be evitable to re-calculate the weight matrix in the network simplification process. As in the matrix factorization methods, the Cholesky factorization factor is calculated by Givens rotation transform to achieve the fast decreasing update of the current connection weight matrix, thus ensuring the numerical stability and high efficiency of the pruning process. Empirical studies on several commonly used classification benchmark problems and the real datasets collected from coal industry show that compared with the traditional ELM algorithm, the pruning multilayered ELM algorithm proposed in this paper can find the optimal number of hidden nodes automatically and has better generalization performance.

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Section: Multilayered Elm (Melm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Liu

et al. 2021

Mathematical Problems in Engineering

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“…The multilayer ELM-LRF is another known ELM-LRF variation which consists of multiple convolution and pooling layers [67], [27], [38], [51], [114], and [77].…”

Section: Random Filtermentioning

confidence: 99%

“…Autoencoding ELM-LRF proposes high-level feature representation using ELM-AE with ELM-LRF and is proposed by [27], [28], and [93]. Another notable difference is using three ELM-AE used in parallel for each respective colour channel for coding features.…”

Section: Random Filtermentioning

confidence: 99%

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Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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A hybrid deep learning CNN-ELM approach for parking space detection in Smart Cities

Kaur

Roul

Batra

2023

Neural Comput & Applic

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Extreme learning machine with autoencoding receptive fields for image classification

Cited by 8 publications

References 21 publications

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Convolutional Extreme Learning Machines: A Systematic Review

A hybrid deep learning CNN-ELM approach for parking space detection in Smart Cities

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