High-Performance Visual Tracking With Extreme Learning Machine Framework

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

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A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

Unmanned Systems ◽

10.1142/s2301385015400038 ◽

2015 ◽

Vol 03 (04) ◽

pp. 267-275

Author(s):

Liang Dai ◽

Yuesheng Zhu ◽

Guibo Luo ◽

Chao He ◽

Hanchi Lin

Keyword(s):

Real Time ◽

Extreme Learning Machine ◽

Visual Tracking ◽

Computational Cost ◽

Real Time Tracking ◽

Sparse Autoencoder ◽

Selection For ◽

Learning Machine ◽

Hidden Layer ◽

Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

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DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC

PeerJ Computer Science ◽

10.7717/peerj-cs.411 ◽

2021 ◽

Vol 7 ◽

pp. e411

Author(s):

Osman Altay ◽

Mustafa Ulas ◽

Kursat Esat Alyamac

Keyword(s):

Extreme Learning Machine ◽

Discrete Time ◽

High Performance ◽

Chaotic Systems ◽

Optimization Methods ◽

Generalized Solution ◽

Support Vector ◽

Classification Problems ◽

Regression Problems ◽

Learning Machine

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.

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Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Materials ◽

10.3390/ma13051023 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1023

Author(s):

Abobakr Khalil Al-Shamiri ◽

Tian-Feng Yuan ◽

Joong Hoon Kim

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Prediction Model ◽

Extreme Learning Machine ◽

High Performance ◽

High Performance Concrete ◽

Fine Aggregate ◽

Input Variables ◽

Prediction Reliability ◽

Learning Machine

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

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Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine

Energies ◽

10.3390/en12234504 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4504 ◽

Cited By ~ 2

Author(s):

Teke Gush ◽

Syed Basit Ali Bukhari ◽

Khawaja Khalid Mehmood ◽

Samuel Admasie ◽

Ji-Soo Kim ◽

...

Keyword(s):

Extreme Learning Machine ◽

High Performance ◽

Test System ◽

Operating Conditions ◽

Fault Classification ◽

Identification Method ◽

Kernel Extreme Learning Machine ◽

Location Identification ◽

Learning Machine ◽

Stockwell Transform

This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

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Multitask Extreme Learning Machine for Visual Tracking

Cognitive Computation ◽

10.1007/s12559-013-9242-z ◽

2014 ◽

Vol 6 (3) ◽

pp. 391-404 ◽

Cited By ~ 11

Author(s):

Huaping Liu ◽

Fuchun Sun ◽

Yuanlong Yu

Keyword(s):

Extreme Learning Machine ◽

Visual Tracking ◽

Learning Machine

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Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.2016102.68266 ◽

2017 ◽

Vol 10 (2) ◽

pp. 196-204

Author(s):

Khanittha Phumrattanaprapin ◽

Punyaphol Horata

Keyword(s):

Deep Learning ◽

Extreme Learning Machine ◽

Multilayer Perceptron ◽

High Performance ◽

Single Layer ◽

Classification Performance ◽

Classification Problems ◽

Hierarchical Learning ◽

Deep Architecture ◽

Learning Machine

The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. However, there still remains room to enhance its classification performance. This paper therefore proposes a new method termed the extending hierarchical extreme learning machine (EH-ELM), which extends the number of layers in the supervised portion of the H-ELM from a single layer to multiple layers. To evaluate the performance of the EH-ELM, the various classification datasets were studied and compared with the H-ELM and the multilayer ELM, as well as various state-of-the-art such deep architecture methods. The experimental results show that the EH-ELM improved the accuracy rates over most other methods.

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