scholarly journals Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 13433-13444 ◽  
Author(s):  
Hongwei Ge ◽  
Weiting Sun ◽  
Mingde Zhao ◽  
Yao Yao
Keyword(s):  
Extreme Learning Machine ◽  
Graph Embedding ◽  
Feature Representation ◽  
Learning Machine

Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

2019 ◽  
Vol 14 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Ji-Yong An ◽  
Yong Zhou ◽  
Lei Zhang ◽  
Qiang Niu ◽  
Da-Fu Wang
Keyword(s):  
Extreme Learning Machine ◽  
Functional Organization ◽  
Web Server ◽  
Feature Representation ◽  
Experimental Results ◽  
Evolutionary Information ◽  
Svm Classifier ◽  
Interacting Proteins ◽  
Learning Machine ◽  
Human Dataset

Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell. Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation. Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. </P><P> Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-theart methods. Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

scholarly journals Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

2019 ◽  
Vol 9 (3) ◽  
pp. 614 ◽  
Author(s):  
Baoxian Wang ◽  
Yiqiang Li ◽  
Weigang Zhao ◽  
Zhaoxi Zhang ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Damage Detection ◽  
Extreme Learning Machine ◽  
Crack Detection ◽  
Feature Learning ◽  
Feature Representation ◽  
Fine Tuning ◽  
Detection Model ◽  
Concrete Crack ◽  
Learning Machine ◽  
Sparse Feature Representation

Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

GenELM: Generative Extreme Learning Machine feature representation

2019 ◽  
Vol 362 ◽  
pp. 41-50 ◽  
Author(s):  
Shichao Zhou ◽  
Chenwei Deng ◽  
Wenzheng Wang ◽  
Guang-Bin Huang ◽  
Baojun Zhao
Keyword(s):  
Extreme Learning Machine ◽  
Feature Representation ◽  
Learning Machine
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