Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System

Yi, Kyu Yang; Wang, Xuesong; Cheng, Yuhu; Chen, C. L. Philip

doi:10.3390/rs10050685

Cited by 79 publications

(32 citation statements)

References 28 publications

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“…where λ = 0, Equation (19) degenerates into the least square problem, but if λ → ∞ , the solution is heavily constrained and tends to 0. So, we refer to BLS and set λ = 2 −30 [30]. By giving an approximation to the Moore-Penrose generalized inverse of [Z s |H s ], Equation (19) can be written as:…”

Section: Broad Learning Systemmentioning

confidence: 99%

“…(1) Dataset IVa, BCI competition III [32] In view of the particularity and complexity of the original EEG signals, it was necessary to perform EEG data preprocessing. For each subject, a time window of 0.5 s~2 s was selected for EEG data extraction, and then a 5th-order Butterworth filter was used to perform band-pass filtering operation of 8~30 Hz [30]. Next, the EEG signals were reduced in dimension using the CSP algorithm.…”

Section: Bci Datasetsmentioning

confidence: 99%

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Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

et al. 2019

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In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals.

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Section: Broad Learning Systemmentioning

confidence: 99%

Section: Bci Datasetsmentioning

confidence: 99%

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

et al. 2019

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“…Finally, all mapped features and EN are directly connected to the output, the corresponding output coefficients can be derived from pseudoinverse. 30 BLS has been successfully applied to some specific image classification tasks, 30,33 which outperformed common classifiers with limited labeled samples, such as k-nearest neighbor (KNN), 34 support vector machine (SVM), 35 and extreme learning machine (ELM). 36 However, most of the real problems relevant to regression and classification are complex and need very broad-scale feature nodes, leading to extremely long training time.…”

Section: Introductionmentioning

confidence: 99%

Depth-based human activity recognition via multi-level fused features and fast broad learning system

Yao

Meng-ting

Chen

et al. 2020

International Journal of Distributed Sensor Networks

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Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. First, the surface normals are computed from original depth maps; the histogram of the surface normal orientations is obtained as a low-level feature by accumulating the contributions from normals, then a high-level feature is acquired by sparse coding and pooling on the aggregation of polynormals. After that, the principal component analysis is applied to the conjunction of the two-level features in order to obtain a lowdimensional and discriminative fused feature. At last, fast broad learning system based on matrix decomposition is proposed to accelerate the training process and enhance the classification results. The recognition results on three benchmark data sets show that our method outperforms the state-of-the-art methods in term of accuracy, especially when the number of training samples is small.

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“…Image recognition, which aims to determine the labels of those query samples, has attracted the attention of many scholars in the machine learning community [1], [2]. In many computer version applications, ranging from hyperspectral image analysis [3], image denoising [4] to face recognition [5], image recognition plays an important and fundamental role. Due to the uncontrollable factors such as illumination and occlusion which commonly occur in images, designing effective and efficient recognition methods is still a challenging and urgent topic [6].…”

Section: Introductionmentioning

confidence: 99%

A New Local Knowledge-Based Collaborative Representation for Image Recognition

Jin

Sun

et al. 2020

IEEE Access

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Recently, collaborative representation based classifiers (CRC) have shown outstanding performances in recognition tasks. The key to success of most CRC algorithms states that the testing samples can be coded well by a suitable dictionary globally, while the local knowledge between samples has not been fully considered. We observe that the representations of similar samples have a high degree of similarity. In order to take advantage of this important similarity information, this paper proposes a new local knowledge-based collaborative representation model for image classification. Specifically, certain adjacent training samples of the testing image should be determined firstly, and then the representations of these neighborhoods can be applied to guide the coefficients of the testing samples to be more discriminative. Further, we derive a robust version of the proposed method to treat the face recognition with occlusions or corruptions. Extensive experiments are carried out to show the superiority of the proposed method over other state-of-the-art classifiers on various image recognition tasks.

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Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System

Cited by 79 publications

References 28 publications

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning

Depth-based human activity recognition via multi-level fused features and fast broad learning system

A New Local Knowledge-Based Collaborative Representation for Image Recognition

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