Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Shi, Guangyao; Luo, Fulin; Tang, Yiming; Li, Yuan

doi:10.3390/rs13071363

Cited by 27 publications

(10 citation statements)

References 68 publications

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“…This is because of the ability of HSI to perceive biochemical and morphological changes associated with the disorder. Furthermore, HSI has been used in other fields such as remote sensing [ 27 ], where high dimensional hyperspectral images produce difficulties for land cover classification and dimensionality reduction methods have been proven to help in HSI classification [ 28 ]. Additionally, HSI has also been used in food quality analysis [ 29 ], among other areas [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Urbanos

Martin

Vázquez

et al. 2021

Sensors

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Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

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Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Urbanos

Martin

Vázquez

et al. 2021

Sensors

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“…In [24], a hybrid-graph discriminant learning method was proposed to reveal the complex highorder relationship of the original data and to extract the discriminant features. In [25], a local constrained manifold structure collaborative preserving embedding was proposed to enhance the intraclass compactness and the interclass separability. In [26], to improve the classification performance of motor imagery, a sparse Bayesian ELM algorithm was proposed to automatically control the model complexity and exclude redundant hidden neurons.…”

Section: Introductionmentioning

confidence: 99%

Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

Zhang

Hou

Tang

et al. 2021

Journal of Control Science and Engineering

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In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

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“…However, these approaches ignore temporal patterns and require the retrieval of a large amount of linguistic information from responsive comments. Graph embedding, a major component of graph learning [24], [25] and graph analysis [26] to represent graphs as low-dimensional vectors, have also been considered for propagation tree representation (e.g. node2vec [27] used in [13]).…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Fusion for Rumor Detection on Twitter

Luo

Zheng³

2021

IEEE Access

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The increasing popularity of social media has made the creation and spread of rumors much easier. Widespread rumors on social media could cause devastating damages to society and individuals. Automatically detecting rumors in a timely manner is greatly needed but also very challenging technically. In this paper, we propose a new deep feature fusion method that employs the linguistic characteristics of the source tweet text and the underlying patterns of the propagation tree of the source tweet for Twitter rumor detection. Specifically, the pre-trained Transformer-based model is applied to extract context-sensitive linguistic features from the short source tweet text. A novel sequential encoding method is proposed to embed the propagation tree of a source tweet into the vector space. A convolutional neural network (CNN) architecture is then developed to extract temporal-structural features from the encoded propagation tree. The performance of the proposed deep feature fusion method is evaluated with two public Twitter rumor datasets. The results demonstrate that the proposed method achieves significantly better detection performance than other state-of-the-art baseline methods.

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Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding

Cited by 27 publications

References 68 publications

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

Deep Feature Fusion for Rumor Detection on Twitter

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