Feng S, Duarte MF (2018) Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation. Neurocomputing 312:310–323 Google Scholar Zhang Z, Yiyang Tian L, Bai JX, Hancock E (2017) High-order covariate interacted Lasso for feature selection. Pattern Reco...
3.3. Low-Rank Representation Based on Dual Graph Regularization In the previous methods based on LRR, for one thing, they always focus on the global Euclidean structure in the spatial feature, and ignore the importance of local geometric structure; for another, their utilization of spectral informa...
Common algorithms include sparse autoencoders and variational autoencoders. Good classification results have been obtained using this strategy. However, it is too time-consuming. Notably, semi-supervised graph convolutional networks (SSGCNs) have demonstrated notable efficacy as one of the most ...
proposed an improved SAE, namely spatial updated deep autoencoder, and updated the features in consideration of the contextual information [25]. Moreover, Chen et al. also verified the eligibility of deep belief networks (DBN) in the HSI spectral-spatial analysis [26]. However, both SAE and ...
2.1.1. Construction of Hybrid Spatial–Spectral Graph The input original HSI data cube can be represented as a three-order data cube 𝐇∈ℝ𝑊×𝐶×𝐵H∈ℝW×C×B, in which W, C, and B represent the numbers of rows, columns, and bands of the original HSI, respectively. Befor...
based on image-to-image translation techniques in image generation, with structures including Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs); there are also some unique algorithms based on other graph theory principles, such as the shortest path ...
[1,2,3], including multilayer perceptron (MLP) [4], stacked autoencoders (SAEs) [5], deep belief networks (DBNs) [6], recurrent neural networks (RNNs) [7,8,9], convolutional neural networks (CNNs) [10,11,12], graph convolutional networks (GCNs) [13], generative adversarial ...
Fusion of hyperspectral and lidar data using sparse and low-rank component analysis. IEEE Trans. Geosci. Remote Sens. 2017, 55, 6354–6365. [Google Scholar] [CrossRef] Gader, P.; Zare, A.; Close, R.; Aitken, J.; Tuell, G. MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set;...
Furthermore, given the potential of graph neural networks for denoising tasks, future research could also explore their application in this context. 5. Conclusions This study proposes DHCT-GAN, a dual-branch network that integrates cross-domain knowledge for enhancing the denoising of EEG signals. ...
Mainstream deepfake detection algorithms generally fail to fully extract forgery traces and have low accuracy when detecting forged images with natural corruptions or human damage. On this basis, a new algorithm based on an adversarial dual-branch data augmentation framework and a modified attention mech...