Multi-label learning method based on ML-RBF and laplacian ELM

Xiaoli, Xu; Dong, Shan; Li, Shan; Sun, Tongfeng; Xiao, Pengcheng; Fan, Jianping

doi:10.1016/j.neucom.2018.11.018

Cited by 24 publications

(10 citation statements)

References 20 publications

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“…Multi-label learning method could also use the multi-label radial basis function neural network and Laplacian ELM (Xu et al. 2019 ), in this algorithm, clustering algorithm determines the number of hidden nodes, and the center of the activation function could be determined by the data itself, then the output is solved by a Laplacian ELM. Inspired by biological intelligent systems, bio-inspired learning model blooms a lot recently (Huang and Chen 2016 ; Alencar et al.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

Zhao

et al. 2022

Soft Comput

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Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learn the sub-structure by three sub-structures’ algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning is implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

Zhao

et al. 2022

Soft Comput

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“…Yan [203] adopted both ELM and RBF to achieve higher accuracy of prediction in their experiments. And research of similar direction was concerned constantly in recent years [65,201].…”

Section: Elm Vs Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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“…Another group of methods is to modify the algorithms, like RankSVM [8], using ranking loss and maximizing the ''margin'' between the related labels and others. Other typical modified algorithms for multi-label tasks include ML-RBF [23], ML-kNN [24] and ML-ELM [25]. Deep Neural Network(DNN)-based models are also belonging to this group and the most common strategy is adjusting the last softmax layer, then adopting binary crossentropy(BCE) loss [26] for the network.…”

Section: Related Work a Multi-label Classificationmentioning

confidence: 99%

Bi-Modal Learning With Channel-Wise Attention for Multi-Label Image Classification

2020

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Multi-label image classification is more in line with the real-world applications. This problem is difficult due to the the fact that complex label space makes it hard to get label-level attention regions and deal with semantic relationships among labels. Common deep network-based methods utilize CNN to extract features and consider the labels as a sequence or a graph, thus handling the label correlations with RNN or graph-theoretical algorithms. In this paper, we propose a novel CNN-RNN-based model, bi-modal multi-label learning(BMML) framework. Firstly, an improved channel-wise attention mechanism is presented to propose regional attention maps and connect them to relative labels. After that, based on the assumption that objects in a semantic scene always have high-level relevance among visual and textual corpus, we further embed the labels through different pre-trained language models and determine the label sequence in a ''semantic space'' constructed on large-scale textual data, thereby handling the labels in their semantic context. In addition, a cross-modal feature aligning module is introduced in BMML framework. Experimental results show that BMML is able to achieve better accuracies then those mainstream multi-label classification methods on several benchmark data sets. INDEX TERMS Multi-label image classification, multi-modal learning, label embedding, deep learning, attention.

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Multi-label learning method based on ML-RBF and laplacian ELM

Cited by 24 publications

References 20 publications

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

A review on extreme learning machine

Bi-Modal Learning With Channel-Wise Attention for Multi-Label Image Classification

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