scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

scholarly journals A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1284
Author(s):  
Licheng Cui ◽  
Huawei Zhai ◽  
Hongfei Lin
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Weight Matrix ◽  
Testing Time ◽  
Least Squares Regression ◽  
Classification Problems ◽  
Training Time ◽  
Discrimination Ability ◽  
Learning Machine ◽  
Hidden Layer

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.

scholarly journals Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuefan Xu ◽  
Sen Zhang ◽  
Zhengtao Cao ◽  
Qinqin Chen ◽  
Wendong Xiao
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Time Domain ◽  
Heartbeat Classification ◽  
Time Frequency ◽  
Rr Interval ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Frequency Features

Automatic heartbeat classification via electrocardiogram (ECG) can help diagnose and prevent cardiovascular diseases in time. Many classification approaches have been proposed for heartbeat classification, based on feature extraction. However, the existing approaches face the challenges of high feature dimensions and slow recognition speeds. In this paper, we propose an efficient extreme learning machine (ELM) approach for heartbeat classification with multiple classes, based on the hybrid time-domain and wavelet time-frequency features. The proposed approach contains two sequential modules: (1) feature extraction of heartbeat signals, including RR interval features in the time-domain and wavelet time-frequency features, and (2) heartbeat classification using ELM based on the extracted features. RR interval features are calculated to reflect the dynamic characteristics of heartbeat signals. Discrete wavelet transform (DWT) is used to decompose the heartbeat signals and extract the time-frequency features of the heartbeat signals along the timeline. ELM is a single-hidden layer feedforward neural network with the hidden layer parameters randomly generated in advance and the output layer parameters calculated optimally using the least-square algorithm directly using the training samples. ELM is used as the heartbeat classification algorithm due to its high accuracy training accuracy, fast training speed, and good generalization ability. Experimental testing is carried out using the public MIT-BIH arrhythmia dataset to perform a 16-class classification. Experimental results show that the proposed approach achieves a superior classification accuracy with fast training and recognition speeds, compared with existing classification algorithms.

scholarly journals Improve Efficiency of Extreme Learning Machine based on Chaos Particle Swarm Optimization Method

2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Optimization Method ◽  
Running Speed ◽  
Swarm Optimization ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.

Multi-feature learning-based extreme learning machine for rolling bearing fault diagnosis

2021 ◽  
pp. 1748006X2110485
Author(s):  
Longkui Zheng ◽  
Yang Xiang ◽  
Chenxing Sheng
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Feature Learning ◽  
Rolling Bearing ◽  
Working Environment ◽  
Learning Methods ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learning Machine ◽  
Hidden Layer

Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.

scholarly journals Classification of Parkinson’s disease patients based on spectrogram using local binary pattern descriptors

2022 ◽  
Vol 2153 (1) ◽  
pp. 012014
Author(s):  
E Gelvez-Almeida ◽  
A Váasquez-Coronel ◽  
R Guatelli ◽  
V Aubin ◽  
M Mora
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Extreme Learning Machine ◽  
Local Binary Pattern ◽  
Audio Signal ◽  
Training Time ◽  
Network Training ◽  
Learning Machine ◽  
Hidden Layer ◽  
Rate Of Success

Abstract Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classification between Parkinson’s disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary Pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers.

scholarly journals Rank based Pseudoinverse Computation in Extreme Learning Machine for large Datasets

2019 ◽  
Vol 8 (10) ◽  
pp. 1341-1346
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Matrix Decomposition ◽  
Least Square ◽  
Feed Forward Neural Network ◽  
Worst Case ◽  
Training Time ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Nodes

Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.

scholarly journals A Vortex Identification Method Based on Extreme Learning Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jun Wang ◽  
Lei Guo ◽  
Yueqing Wang ◽  
Liang Deng ◽  
Fang Wang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Flow Field ◽  
Extreme Learning Machine ◽  
Vortex Identification ◽  
Learning Methods ◽  
Training Time ◽  
Identification Methods ◽  
Identification Method ◽  
Learning Machine ◽  
High Computational Complexity

Vortex identification and visualization are important means to understand the underlying physical mechanism of the flow field. Local vortex identification methods need to combine with the manual selection of the appropriate threshold, which leads to poor robustness. Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. It cannot be extended and is suitable for flow fields of different sizes. Recently, proposed deep learning methods have long network training time and high computational complexity. Aiming at the above problems, we present a novel vortex identification method based on the Convolutional Neural Networks-Extreme Learning Machine (CNN-ELM). This method transforms the vortex identification problem into a binary classification problem, and can quickly, objectively, and robustly identify vortices from the flow field. A large number of experiments prove the effectiveness of our method, which can improve or supplement the shortcomings of existing methods.

Sign in / Sign up

Export Citation Format

Share Document