Journal ArticleDOI
Representational learning with ELMs for big data
TLDR
Huang et al. as mentioned in this paper proposed ELM-AE, a special case of ELM, where the input is equal to output, and the randomly generated weights are chosen to be orthogonal.Abstract:
Geoffrey Hinton and Pascal Vincent showed that a restricted Boltzmann machine (RBM) and auto-encoders (AE) could be used for feature engineering. These engineered features then could be used to train multiple-layer neural networks, or deep networks. Two types of deep networks based on RBM exist: the deep belief network (DBN)1 and the deep Boltzmann machine (DBM). Guang-Bin Huang and colleagues introduced the extreme learning machine (ELM) as an single-layer feed-forward neural networks (SLFN) with a fast learning speed and good generalization capability. The ELM for SLFNs shows that hidden nodes can be randomly generated. ELM-AE output weights can be determined analytically, unlike RBMs and traditional auto-encoders, which require iterative algorithms. ELM-AE can be seen as a special case of ELM, where the input is equal to output, and the randomly generated weights are chosen to be orthogonal.read more
Citations
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Journal ArticleDOI
Application of Deep Wavelet Kernel Extreme Learning Machine in Fault Diagnosis of Tamping Vehicle
Hairui Wang,Guifu Zhu,Xiuqi Yang +2 more
TL;DR: Wang et al. as mentioned in this paper introduced the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones.
Proceedings ArticleDOI
Random Generated Dictionaries for Convolutional Sparse Coding: An ELM Interpretation for Simple CSC Applications
TL;DR: In this paper , the authors explored the usefulness of using a randomly generated filterbank (FB) as the convolutional dictionary in CNN representations and assessed its performance for simple applications such as denoising and super resolution.
Journal ArticleDOI
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
Leonardo F. S. Scabini,Kallil M. C. Zielinski,Lucas Correia Ribas,Wesley Nunes Gonçalves,Bernard De Baets,Odemir Martinez Bruno +5 more
TL;DR: In this paper , a Randomized Autoencoder (RAE) is used to encode the output at different depths of a pre-trained deep convolutional network using a closed-form solution, and its decoder weights are used to compose a 1-dimensional texture representation.
Proceedings ArticleDOI
An Extreme Learning Machine Based Pretraining Method for Multi-Layer Neural Networks
TL;DR: A new ELM based unsupervised learning, named backward ELMbased autoencoder (BELM-AE), to pretrain each layer of a neural network before using a back-propagation based learning algorithm to fine-tune the whole network.
References
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Journal ArticleDOI
Extreme learning machine: Theory and applications
TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.
Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
Universal approximation using incremental constructive feedforward networks with random hidden nodes
TL;DR: This paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer.
Journal ArticleDOI
Optimization method based extreme learning machine for classification
TL;DR: Under the ELM learning framework, SVM's maximal margin property and the minimal norm of weights theory of feedforward neural networks are actually consistent and ELM for classification tends to achieve better generalization performance than traditional SVM.