Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the autoencoder (AE) model, as a baseline network for unmixing,...
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ISBN:
(纸本)9783031414558;9783031414565
Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the autoencoder (AE) model, as a baseline network for unmixing, performs well in Hyperspectral Unmixing (HU) by automatically learning a new representation and recovering original data. However, patch-wise AE based architecture, which incorporates both spectral and spatial information through convolutional filters may blur the abundance maps due to the fixed kernel shape of the used window size. To cope with the above issue, we propose in this paper a novel methodology based on graph DL called DNGAE. Unlike the pixel-wise or patch-wise Convolutional AE (CAE), our proposed method incorporates the complementary spatial information based on graph spectral similarity. A neighborhood graph based on band correlations is firstly constructed. Then, our method attempts to aggregate similar spectra from the neighboring pixels of a target pixel. Consequently, this leads to better quality of both extracted endmembers and abundances. Extensive experiments performed on two real HSI benchmarks confirm the effectiveness of our proposed method compared to other DL models.
Single-cell RNA-sequencing (scRNA-seq) technology has revolutionized the field by enabling the profiling of transcriptomes in cell resolution. However, it is flawed by the sparsity caused by low mRNA capture efficienc...
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Unlike traditional clustering analysis,the biclustering algorithm works simultaneously on two dimensions of samples(row)and variables(column).In recent years,biclustering methods have been developed rapidly and widely...
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Unlike traditional clustering analysis,the biclustering algorithm works simultaneously on two dimensions of samples(row)and variables(column).In recent years,biclustering methods have been developed rapidly and widely applied in biological data analysis,text clustering,recommendation system and other *** traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale *** present,most of the biclustering algorithms are designed for the differentially expressed big biological ***,there is little discussion on binary data clustering mining such as miRNA-targeted gene ***,we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as *** applies graph autoencoder to capture the similarity of sample sets or variable sets,and takes a new irregular clustering strategy to mine biclusters with excellent *** on the miRNA-targeted gene data of soybean,we benchmark several different types of the biclustering algorithm,and find that GAEBic performs better than Bimax,Bibit and the Spectral Biclustering algorithm in terms of target gene *** biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.
Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effecti...
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Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns selfsupervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential.
Self-supervised graph learning has attracted significant interest, especially graph contrastive learning. However, graph contrastive learning heavily relies on the choices of negative samples and the elaborate designs...
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ISBN:
(纸本)9783031208645;9783031208652
Self-supervised graph learning has attracted significant interest, especially graph contrastive learning. However, graph contrastive learning heavily relies on the choices of negative samples and the elaborate designs of architectures. Motivated by Barlow Twins, a method in computer vision, we propose a novel graph autoencoder named Core Barlow graph Auto-Encoder(CBGAE) which does not rely on any special techniques, like predictor networks or momentum encoders. Meanwhile, we set a core view to make maximize agreement between the learned feature information. In contrast to the most existing graph contrastive learning models, it is negative-sample-free.
graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to lower the computational cost. Researchers have developed different graph autoenc...
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graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to lower the computational cost. Researchers have developed different graph autoencoders for addressing different needs. This paper proposes a strategy based on noise injection for graph autoencoder training. This is a general training strategy that can flexibly fit most existing training algorithms. The experimental results verify this general strategy can significantly reduce overfitting and identify the noise rate setting for consistent training performance improvement.
Remaining useful life (RUL) of lithium-ion battery is important to maintain safe and reliable battery operation. Health indicators (HIs) are key features for predicting RUL during battery aging, whereas current method...
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Remaining useful life (RUL) of lithium-ion battery is important to maintain safe and reliable battery operation. Health indicators (HIs) are key features for predicting RUL during battery aging, whereas current methods only consider their link to capacity. In order to learn the intrinsic connection between the aging features, a RUL prediction method based on multi decoder graph autoencoder (MGAE) and transformer network is proposed, which considers both the link between aging characteristics and the link between aging characteristics and capacity degradation. First, multiple types of aging features are extracted during battery charging and discharging, and HIs are connected into a graph structure by pearson correlation analysis. Thereby, feature information with high correlation is linked through the topology of the graph. Subsequently, the feature graph and feature matrix are input to the graph autoencoder to extract deep features. In graph decoder part, this paper improves to a multi decoder in order to update and select features by the updated graph structure. Finally, new feature matrix is fed into transformer, and RUL prediction is realized by parallel processing through multi-head self-attention. The validity of proposed method is demonstrated by NASA dataset and compared with other advanced methods. The results show that our approach achieves average RE of 0.09 and maintains RMSE of 0.01.
Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only lear...
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ISBN:
(纸本)9781450391320
Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.
Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The...
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Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The key challenge is that deep learning based pricing models require initial cascade graphs as inputs to predict influence, which cannot be obtained while pricing nodes. Furthermore, node pricing must enhance purchase intentions while being consistent with their influence. To address these challenges, a nodepricing framework is proposed, in which market price is determined based on the predicted influence. In this framework, corrections are performed by using a graph autoencoder (GAE). The corrections are used to augment the neighborhood subgraph and facilitate the extraction of valid sequence features, which are then used to predict influence. An approximate Shapley value for node influence is used to evaluate the price of the nodes. A multi -perspective pricing approach is further investigated, where consumer utility and the approximate Shapley value for influence are the objectives. An inflection point is chosen on the Pareto frontier to select a price that enhances consumer utility. Extensive experiments were conducted on two real -world social network datasets. The results indicate that our performance is higher than DeepCas by 10.38% in Twitter and 9.64% in Weibo . The price output by our framework is consistent with the nodes' social marketing value while maximizing consumer utility.
Single-cell ribonucleic acid sequencing (scRNA-seq) allows researchers to study cell heterogeneity and diversity at the individual cell level. Cell clustering is an essential component of scRNA-seq data processing. Ho...
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Single-cell ribonucleic acid sequencing (scRNA-seq) allows researchers to study cell heterogeneity and diversity at the individual cell level. Cell clustering is an essential component of scRNA-seq data processing. However, the high dimensionality and high noise characteristics of scRNA-seq data may pose problems during data processing. Although many methods are available for scRNA-seq clustering analysis, most of them ignore the topological relationships of scRNA-seq data and do not fully utilize the potential associations between cells. In this study, we present scGAD, a graph attention autoencoder model with a dual decoder structure for clustering scRNA-seq data. We utilize a graph attention autoencoder with two decoders to learn feature representations of cells in latent space. To ensure that the learned latent feature representation maintains node properties and graph structure, we use an inner product decoder and a learnable graph attention decoder to reconstruct graph structure and node properties, respectively. On the 12 real scRNA-seq datasets, the average NMI and ARI scores of scGAD are 0.762 and 0.695, respectively, outperforming state-of-the-art single-cell clustering approaches. Biological analysis shows that the cell labels predicted by scGAD can assist in the downstream analysis of scRNA-seq data.
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