Multi-scale deep multi-view subspace clustering with self-weighting fusion and structure preserving

Wang, Jiao; Wu, Bin; Ren, Zhenwen; Zhang, Hongying; Zhou, Yunhui

doi:10.1016/j.eswa.2022.119031

Cited by 19 publications

(3 citation statements)

References 41 publications

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“…This approach offers an unprecedented level of spatial granularity, encompassing thousands of specific locations [26]. Wang et al introduced a novel PM 2.5 spatiotemporal forecasting framework, in which a mixed graph convolutional network is utilized to extract spatial features, and a secondorder residual temporal convolutional network is developed to capture temporal features [34]. Xu et al presented a dynamic graph neural network with adaptive edge attributes for air quality prediction, which is capable of adaptively learning the correlation between real sites and achieve better time series prediction results [35].…”

Section: Related Workmentioning

confidence: 99%

Adversarial transfer learning based hybrid recurrent network for air quality prediction

Hao,

Luo,

et al. 2024

Preprint

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Air quality modeling and forecasting has become a key problem in environmental protection. The existing prediction models typically require large-scale and high-quality historical data to achieve better performance. However, insufficient data volume and significant differences between data distribution across different regions will definitely reduce the effectiveness of the model reuse. To address the above issues, we propose a novel hybrid recurrent network based on domain adversarial transfer to achieve a stronger generalization ability when training air quality data from multi-source domains. The proposed model mainly consists of three fundamental modules, i.e., feature extractor, regression predictor, and domain classifier. One-dimensional convolutional neural networks (1D-CNNs) are used to extract temporal feature of data from source and target stations. Bi-directional gated recurrent unit (bi-GRU) and bi-directional long short-term memory (bi-LSTM) are utilized to learn temporal dependencies pattern of multivariate time series data. Two adversarial transfer strategies are employed to ensure that our model is capable of finding domain invariant representations automatically. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed domain transfer strategies. The experimental results also show that our composite model has superior performance for forecasting air quality in various regions. As further evidence, the adversarial training method could promote the positive transfer and alleviate the negative effect of irrelevant source data. Besides, our model exhibits preferable generalization capability as more robust prediction results are achieved on both unseen target domains and original source domains.

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Section: Related Workmentioning

confidence: 99%

Adversarial transfer learning based hybrid recurrent network for air quality prediction

Hao,

Luo,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…All the view-specific self-expressions are fused to obtain the consensus self-expression by a fully connected layer [25]. In [17], the view-specific self-expressions are weighted by the channel attention mechanism and fused by the convolution kernel to learn consensus self-expression with maximum complementarity for precise clustering. Information bottleneck is extended to explore view-specific information in the latent feature space.…”

Section: Related Work 21 Deep Multi-view Subspace Clusteringmentioning

confidence: 99%

“…Recently, deep multi-view subspace clustering (DMVSC) methods have been proposed due to the powerful high-dimensional nonlinear representation ability [16,17]. DMVSC methods project the high-dimensional nonlinear data into low-dimensional latent features by deep model and then learn the consensus self-expression of multi-view data based on feature space.…”

Section: Introductionmentioning

confidence: 99%

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Wang,

Wu,

Zhang

2024

CMC

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Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivate meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpression layer embedded into the auto-encoder network for each view. To guarantee consistency across views and reduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastive learning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according to their discrimination. Then they are fused by convolution kernel to obtain consensus self-expression with maximum complementarity of multiple views. Extensive experimental results on four benchmark datasets and one large-scale dataset of the CCAC method outperform other state-of-the-art methods, demonstrating its clustering effectiveness.

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Two novel deep multi-view support vector machines for multiclass classification

Li,

Xie

2024

Appl Intell

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Multi-scale deep multi-view subspace clustering with self-weighting fusion and structure preserving

Cited by 19 publications

References 41 publications

Adversarial transfer learning based hybrid recurrent network for air quality prediction

Adversarial transfer learning based hybrid recurrent network for air quality prediction

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Two novel deep multi-view support vector machines for multiclass classification

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