Sparse representation preserving embedding based on extreme learning machine for process monitoring

Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

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Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Computational Intelligence and Neuroscience ◽

10.1155/2021/5570870 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Yusra Khalid Bhatti ◽

Afshan Jamil ◽

Nudrat Nida ◽

Muhammad Haroon Yousaf ◽

Serestina Viriri ◽

...

Keyword(s):

Facial Expression ◽

Extreme Learning Machine ◽

Facial Expressions ◽

Classroom Environment ◽

Facial Expression Recognition ◽

Parameter Tuning ◽

Expression Recognition ◽

Fast Learning ◽

Learning Machine ◽

The Impact

Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.

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Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral–Spatial Classification of Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2828601 ◽

2018 ◽

Vol 56 (11) ◽

pp. 6263-6279 ◽

Cited By ~ 16

Author(s):

Faxian Cao ◽

Zhijing Yang ◽

Jinchang Ren ◽

Wing-Kuen Ling ◽

Huimin Zhao ◽

...

Keyword(s):

Sparse Representation ◽

Extreme Learning Machine ◽

Hyperspectral Images ◽

Spatial Classification ◽

Learning Machine

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HONNs with Extreme Learning Machine to Handle Incomplete Datasets

Artificial Higher Order Neural Networks for Modeling and Simulation ◽

10.4018/978-1-4666-2175-6.ch013 ◽

2013 ◽

pp. 276-292

Author(s):

Shuxiang Xu

Keyword(s):

Neural Networks ◽

Missing Data ◽

Extreme Learning Machine ◽

Real Life ◽

Bankruptcy Prediction ◽

Share Prices ◽

Fast Learning ◽

Business Cases ◽

Learning Machine ◽

Hidden Layer

An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a very fast learning speed: it can learn thousands of times faster than conventionally popular learning algorithms (Huang, et al., 2006). Artificial Neural Networks (ANNs) have been widely used as powerful information processing models and adopted in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents, and many more. Higher Order Neural Networks (HONNs) are ANNs in which the net input to a computational neuron is a weighted sum of products of its inputs. Real life data are not usually perfect. They contain wrong, incomplete, or vague data. Hence, it is usual to find missing data in many information sources used. Missing data is a common problem in statistical analysis (Little & Rubin, 1987). This chapter uses the Extreme Learning Machine (ELM) algorithm for HONN models and applies it in several significant business cases, which involve missing datasets. The experimental results demonstrate that HONN models with the ELM algorithm offer significant advantages over standard HONN models, such as faster training, as well as improved generalization abilities.

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A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2015/260970 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Pengbo Zhang ◽

Zhixin Yang

Keyword(s):

Extreme Learning Machine ◽

Learning Algorithm ◽

Approximation Accuracy ◽

Weak Learner ◽

Fast Learning ◽

Learning Speed ◽

Regression Problems ◽

Learning Machine ◽

The Stability ◽

Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

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FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488513400138 ◽

2013 ◽

Vol 21 (supp02) ◽

pp. 23-34 ◽

Cited By ~ 24

Author(s):

JUNHAI ZHAI ◽

HONGYU XU ◽

YAN LI

Keyword(s):

Extreme Learning Machine ◽

Learning Algorithm ◽

Fuzzy Integral ◽

Fast Learning ◽

Original Dataset ◽

Learning Speed ◽

Feed Forward Neural Networks ◽

Three Stages ◽

Learning Machine ◽

Hidden Layer

Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of original dataset. Secondly, probabilistic SLFNs are trained with ELM algorithm on each subset. Finally, the trained probabilistic SLFNs are fused with fuzzy integral. The experimental results show that the proposed approach can alleviate to some extent the problems mentioned above, and can increase the prediction accuracy.

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Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection

Neural Networks ◽

10.1016/j.neunet.2016.12.002 ◽

2017 ◽

Vol 87 ◽

pp. 109-121 ◽

Cited By ~ 66

Author(s):

Jihun Kim ◽

Jonghong Kim ◽

Gil-Jin Jang ◽

Minho Lee

Keyword(s):

Neural Networks ◽

Extreme Learning Machine ◽

Convolutional Neural Networks ◽

Lane Detection ◽

Learning Method ◽

Fast Learning ◽

Learning Machine

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Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems

Information Sciences ◽

10.1016/j.ins.2011.11.003 ◽

2012 ◽

Vol 187 ◽

pp. 277-290 ◽

Cited By ~ 54

Author(s):

R. Savitha ◽

S. Suresh ◽

N. Sundararajan

Keyword(s):

Extreme Learning Machine ◽

Classification Problems ◽

Fast Learning ◽

Learning Machine ◽

Complex Valued

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A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling

Measurement ◽

10.1016/j.measurement.2020.108186 ◽

2020 ◽

Vol 166 ◽

pp. 108186 ◽

Cited By ~ 1

Author(s):

Yuqing Zhou ◽

Bintao Sun ◽

Weifang Sun

Keyword(s):

Differential Evolution ◽

Extreme Learning Machine ◽

Condition Monitoring ◽

Tool Condition Monitoring ◽

Monitoring Method ◽

Tool Condition ◽

Kernel Extreme Learning Machine ◽

Binary Differential Evolution ◽

Learning Machine

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Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215621585 ◽

2015 ◽

Vol 231 (9) ◽

pp. 1699-1711 ◽

Cited By ~ 7

Author(s):

Fang Liu ◽

Yongbin Liu ◽

Fenglin Chen ◽

Bing He

Keyword(s):

Frequency Domain ◽

Extreme Learning Machine ◽

Life Prediction ◽

Residual Life ◽

Remaining Useful Life ◽

Time Frequency ◽

Fast Learning ◽

Useful Life ◽

Learning Machine ◽

Degradation Indicator

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

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