Restricted Boltzmann machine: a non-linear substitute for PCA in spectral processing

Bu, Yude; Zhao, Gang; Luo, A-Li; Pan, Jingchang; Chen, Yuqin

doi:10.1051/0004-6361/201424194

Cited by 16 publications

(11 citation statements)

References 27 publications

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“…For the RBM (see Appendix A), the hidden layer vectors were similar to the PC vectors given by PCA. We experimentally set the size of the hidden layer of RBM to 500 and the number of epochs to 1000 [27]. We set the DnCNN (see Appendix B) parameters similar to those described in [32].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Lu et al used wavelets to extract features for denoising the stellar spectra by removing higher frequency components [26]. Bu et al proposed a new method called the Restricted Boltzmann machine (RBM) as a substitute to PCA for repairing incomplete spectra, spectral denoising, and spectral dimensionality reduction [27]. To improve the efficiency of spectral pre-processing, Wang et al applied a deep neural network to recover defective spectra [28].…”

Section: Related Workmentioning

confidence: 99%

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Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Pan

et al. 2020

IEEE Access

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Numerous spectra can be obtained from sky surveys such as the Sloan Digital Sky Survey and the Large Sky Area Multi-Object Fibre Spectroscopic Telescope. However, a considerable fraction of such spectra, which are also valuable for astronomical research, are of low quality, possessing characteristics such as low signal-to-noise ratio (low-S/N). Principal component analysis is widely used to process these low-S/N spectra, but it is not efficient enough to describe the non-linear properties within the spectra. Wavelets are often used to denoise the low-S/N spectra. However, as is well known, the most optimal wavelet basis for each type of spectra needs to be determined; therefore, wavelet analysis is very difficult to use in practice. Restricted Boltzmann machine is a non-linear algorithm that performs poorly when applied to low-S/N spectra. Denoising Convolutional Neural Networks (DnCNN) is a promising denoiser, however, its performance is unsatisfactory due to the lack of suitable noise model. To better exploit the spectra with low-S/N, we propose a new method that can be used to obtain better denoised spectra when compared to those obtained using other methods. A new method called the Spectra Generative Adversarial Nets (Spectra-GANs) is introduced. Spectra-GANs is simply a feedforward neural network that learns the difference between the input vector and the target by minimizing the loss function. It can be used in spectral denoising. The performance of Spectra-GANs is better than those of other methods with regard to denoising the spectra, especially with regard to extremely low-S/N spectral processing. Thus, Spectra-GANs proposed herein is a suitable alternative to previously used methods in spectral denoising.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Pan

et al. 2020

IEEE Access

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“…As one of most famous and commonly used neural network models, Restricted Boltzmann Machine (RBM) has been hot in research field since it came out. Bu and Zhao [8] presented a methodology which applied RBM to enhance the potential capacity of extreme learning machine (ELM) in the field of spectral processing. Through their research, it was demonstrated that their joint architecture of RBM and ELM showed better performance than the algorithm of applying principal component analysis (PCA) with ELM learning method.…”

Section: Elm Vs Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

275

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…A DBN can be stacked by a restricted Boltzmann machine (RBM) to extract features efficiently [63,64]. Because the input of a DBN should be a vector and the HSI is a 3D tensor, in this paper, principal component analysis (PCA) is introduced to reduce the dimension of the HSI, and helps to obtain the one-dimensional (1D) input for the DBN.…”

Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

Sun

Yue

et al. 2018

Remote Sensing

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Recent research has shown that spatial-spectral information can help to improve the classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional neural networks (3D-CNNs) have been applied to HSI classification. However, a lack of HSI training samples restricts the performance of 3D-CNNs. To solve this problem and improve the classification, an improved method based on 3D-CNNs combined with parameter optimization, transfer learning, and virtual samples is proposed in this paper. Firstly, to optimize the network performance, the parameters of the 3D-CNN of the HSI to be classified (target data) are adjusted according to the single variable principle. Secondly, in order to relieve the problem caused by insufficient samples, the weights in the bottom layers of the parameter-optimized 3D-CNN of the target data can be transferred from another well trained 3D-CNN by a HSI (source data) with enough samples and the same feature space as the target data. Then, some virtual samples can be generated from the original samples of the target data to further alleviate the lack of HSI training samples. Finally, the parameter-optimized 3D-CNN with transfer learning can be trained by the training samples consisting of the virtual and the original samples. Experimental results on real-world hyperspectral satellite images have shown that the proposed method has great potential prospects in HSI classification.

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Restricted Boltzmann machine: a non-linear substitute for PCA in spectral processing

Cited by 16 publications

References 27 publications

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

Spectra-GANs: A New Automated Denoising Method for Low-S/N Stellar Spectra

A review on extreme learning machine

Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

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