Review of Pseudoinverse Learning Algorithm for Multilayer Neural Networks and Applications

Wang, Jue; Guo, Ping; Xin, Xin

doi:10.1007/978-3-319-92537-0_12

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…However, gradient descent based learning algorithm comes at the expense of several costs such as selection of hyperparameters, having local minima with time consuming iterations, and gradient vanishing. The Moore-Penrose inverse based learning for training the shallow (less than three layers) networks has been recently reviewed in [39]. This approach aims to find the global minima of the reformulated system in a single learning shot without training iterations.…”

Section: Moore-penrose Inverse Based Network Learningmentioning

confidence: 99%

Wi-Fi Based User Identification Using In-Air Handwritten Signature

et al. 2021

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This paper conducts a feasibility study regarding the use of the Wi-Fi channel state information for user recognition based on in-air handwritten signatures. A novel system for identity recognition is thus proposed to observe for distinctive signal distortions along the propagation path for different users. The system capitalizes on the vast availability of Wi-Fi signals for signal analysis without needing additional hardware infra-structure. Since the patterns of the raw Wi-Fi signals are sensitive to the signer's location, a transfer learning has been adopted to cope with the positional variation. Specifically, features trained at one position are transferred to classify signals collected at another position via a single shot retraining. A kernel and range space projection has been adopted for the single shot retraining. Our experiments show encouraging results for the proposed system.

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Section: Moore-penrose Inverse Based Network Learningmentioning

confidence: 99%

Wi-Fi Based User Identification Using In-Air Handwritten Signature

et al. 2021

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“…Especially in the application of the IoT, DNN needs to run on resource-constrained devices, which often have low computing power and can not adapt to a large amount of computing. To overcome the shortcomings mentioned above, researchers have proposed many non-BP based methods, such as pseu-doinverse learning algorithm (PIL) [6,22] and random vector functional link (RVFL) [18].…”

Section: Introductionmentioning

confidence: 99%

DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder

Wang

Guo

2021

Complex Intell. Syst.

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Autoencoder has been widely used as a feature learning technique. In many works of autoencoder, the features of the original input are usually extracted layer by layer using multi-layer nonlinear mapping, and only the features of the last layer are used for classification or regression. Therefore, the features of the previous layer aren’t used explicitly. The loss of information and waste of computation is obvious. In addition, faster training and reasoning speed is generally required in the Internet of Things applications. But the stacked autoencoders model is usually trained by the BP algorithm, which has the problem of slow convergence. To solve the above two problems, the paper proposes a dense connection pseudoinverse learning autoencoder (DensePILAE) from reuse perspective. Pseudoinverse learning autoencoder (PILAE) can extract features in the form of analytic solution, without multiple iterations. Therefore, the time cost can be greatly reduced. At the same time, the features of all the previous layers in stacked PILAE are combined as the input of next layer. In this way, the information of all the previous layers not only has no loss, but also can be strengthened and refined, so that better features could be learned. The experimental results in 8 data sets of different domains show that the proposed DensePILAE is effective.

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“…Pseudoinverse learning algorithm (PIL) [24], [25], it is a multilayer perceptron (MLP) learning algorithm composed of stacked generalization connected such that it dominates the Neural Networks (NNs) degradation predictive accuracy. It's structure possesses identical number of hidden neurons as the number of samples that are to be learned.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

Mahmoud

Guo

2019

IEEE Access

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This paper centers on a novel method for traffic sign recognition (TSR). The method comprises of two major steps: 1) make strong representations for TSR images, by extraction deep features with the deep convolutional generative adversarial networks (DCGANs) and 2) classifier defined by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process is considered efficient in which it does not require the number of hidden layers specified nor does it need the setting of the learning control parameters. This results in the PILAE classifier attaining a better performance in terms of both accuracy and efficiency. Empirical results from the German TSR (GTSRB) and Belgium traffic sign classification (BTSC) have proved that TSR achieves excellent results with other algorithms and reasonably low complexity. INDEX TERMSDeep convolutional generative adversarial networks (DCGAN), feature extraction, pseudoinverse learning autoencoder (PILAE), traffic sign recognition (TSR).

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Review of Pseudoinverse Learning Algorithm for Multilayer Neural Networks and Applications

Cited by 11 publications

References 19 publications

Wi-Fi Based User Identification Using In-Air Handwritten Signature

Wi-Fi Based User Identification Using In-Air Handwritten Signature

DensePILAE: a feature reuse pseudoinverse learning algorithm for deep stacked autoencoder

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

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