A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification

Peng, Yong; Li, Qingxi; Kong, Wanzeng; Qin, Feiwei; Zhang, Jianhai; Cichocki, Andrzej

doi:10.1016/j.asoc.2020.106756

Cited by 37 publications

(29 citation statements)

References 49 publications

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“…A k -nearest neighbor graph is usually included in the Laplacian matrix to define the relationship among the nearby data points ( She et al, 2019b ; Peng et al, 2020 ).…”

Section: Semi-supervised Learningmentioning

confidence: 99%

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Huang

Hua

et al. 2021

Front. Neurosci.

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In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

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“…A k -nearest neighbor graph is usually included in the Laplacian matrix to define the relationship among the nearby data points ( She et al, 2019b ; Peng et al, 2020 ).…”

Section: Semi-supervised Learningmentioning

confidence: 99%

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Huang

Hua

et al. 2021

Front. Neurosci.

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“…A TE simulator can be found in the website: http://brahms.scs.uiuc.edu, which permits one normal operating mode and 21 fault patterns. Note that the fault patterns IDV(3), IDV(9), IDV (15) and IDV (19) have been already proved to be difficultly detected by the data-driven based fault detection approaches because theses fault datasets have no observable changes in the means or the variances [50,52]. Therefore, except for these four fault patterns, the rest of the seventeen fault patterns given in Table 4 are utilized to testify the monitoring capability of the EUELM based scheme in our work.…”

Section: B Case Study On the Te Process (1) Process Descriptionmentioning

confidence: 99%

“…Xie et al [18] discussed a distributed SELM to handle the shortcomings in the timevarying communication network. By combining with the random vector functional link networks, Peng et al [19] developed a joint optimization framework based extension of SELM to use both labeled and unlabeled samples. To improve the effectiveness in disposing non-Gaussian noises, Yang et al [20] proposed a novel SELM method based on robust regularized correntropy criterion.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Shao

Kang

et al. 2021

IEEE Access

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Although the unsupervised extreme learning machine (UELM) based methods have been widely used to diagnosis the nonlinear process faults recently, the UELM algorithm is only designed to preserve the local adjacency similarity of the input dataset instead of mining the intra-class variations. Besides, the determination of the optimal UELM hidden nodes number is a tough issue. In order to deal with these two problems, a novel enhanced UELM (EUELM) based scheme is developed to effectively detect the nonlinear process faults in our work. In the proposed EUELM approach, the UELM algorithm is first improved by naturally incorporating the diversity analysis technique into the original UELM objective function to preserve both the intra-class variation and the local adjacency similarity of the input dataset. Then, to settle the difficult issue of selecting the optimal number of hidden nodes, kernel trick is further employed in the EUELM approach to mine the data strong nonlinearity. Based on the extracted diversity and local similarity low dimensional feature information, the k-nearest neighbor (KNN) principle is applied to derive a monitoring statistic for fault detection. At last, the experiments and comparisons on the monitoring effectiveness of the suggested EUELM based approach are made on a numerical nonlinear system and the benchmark Tennessee Eastman (TE) process. The obtained monitoring results illustrate that the significant improvements can be achieved by the proposed EUELM based fault detection approach compared with other popular and related approaches.

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“…For the frequencydomain features, researchers usually first filter EEG signals into several frequency bands, and then extract EEG features from each frequency band. The frequency interval of interest is 1-50 Hz which is usually partitioned into five frequency bands, Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8)(9)(10)(11)(12)(13)(14), Beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31), and Gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). The frequency-domain features mainly include the differential entropy (DE) feature [14], the power spectral density (PSD) feature [15], the differential asymmetry (DASM) feature [11], the rational asymmetry (RASM) feature …”

Section: Introductionmentioning

confidence: 99%

“…In the education field, it helps to track and improve the learning efficiency of students [ 5 , 6 ]. EEG signals record the neural activities of human cerebral cortex and reflect emotion states, providing an objective and reliable way to perform emotion recognition [ 7 , 8 , 9 ]. Besides, the advantages of EEG such as noninvasive, fast, and inexpensive in data acquisition make it become a preferred media in emotion recognition [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition

Shen

Peng

Kong

et al. 2021

Sensors

Self Cite

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Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the “SEED IV” data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the “DEAP” data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.

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A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification

Cited by 37 publications

References 49 publications

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition

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