Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection

Qu, Jinlong; Zhao, Xiaowei; Xiao, Yun; Chang, Xiaojun; Li, Zhihui; Wang, Xuanhong

doi:10.1016/j.knosys.2023.110411

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…From Equation ( 9), it can be seen that as the samples get closer, their similarity also increases. In addition, there are some similar methods, such as the ϵ-neighborhood method [51] and the fully connected method [52], which can also be utilized to construct graphs.…”

Section: Constructing Graph Methodsmentioning

confidence: 99%

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

Information

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As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.

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Section: Constructing Graph Methodsmentioning

confidence: 99%

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

Information

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“…Graph WaveNet [7] put forwards an adaptive adjacency matrix as a supplement to the priori adjacency matrices. ST-A-PGCL [8] extracts spatiotemporal correlation with missing values through periodical adaptive graph contrastive learning.…”

Section: Graph Neural Network For Traffic Predictionmentioning

confidence: 99%

Traffic flow prediction based on graph wave adaptive spatiotemporal graph convolution network

Diao,

Qu,

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Graph-based traffic flow predictioning is widely applied in traffic systems, where constructing intricate spatiotemporal correlation models from relevant time series data is imperative for comprehending the dynamics of the traffic system. The extraction of features from graphical data, coupled with the integration of time series data, serves to enhance the accuracy of traffic flow predictions. Additionally, the challenge arises when real traffic flow data often contains missing values. Predicting traffic in scenarios with missing data proves to be challenging, as existing traffic flow predictioning methods frequently lack the capacity to model dynamic spatiotemporal correlations in the presence of such gaps, leading to unsatisfactory prediction outcomes. This paper introduces the Adaptive Spatiotemporal Graph WaveNet-based Graph Convolutional Network (AST-GW-GCN) to tackle traffic flow predictioning. AST-GW-GCN comprises three independent components, each modeling short-term, daily, and weekly dependencies of traffic flow. Within each component, Gated Temporal Convolutional Network (TCN) and Graph Convolutional Network (GCN) serve as encoders, conducting spatial convolution to extract spatial correlations and temporal convolution to capture temporal correlations, thereby generating hidden features. The Gated Recurrent Unit (GRU) is employed to decode these hidden features and weigh the outputs of the three components, producing the final prediction result. The spatial convolution module establishes an adaptive adjacency matrix to overcome the physical constraints of the graph structure, facilitating improved extraction of hidden spatial dependencies within the data. Furthermore, experiments are conducted under various data missing patterns and missing ratios. The experimental findings, based on the PeMS08 real dataset, demonstrate that the proposed AST-GW-GCN comprehensively captures spatiotemporal correlations in the data, outperforming baseline models in terms of performance.

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“…Real-world traffic flow data is normally collected from complex sensor systems, essentially introducing noise and data incompleteness issues. Directly training models on such inadequate data could induce notable performance deterioration [20], [42]. Hence, we perform random masking operations on the raw feature matrix X to model the abruptness and unpredictability of diverse data incompleteness, thereby strengthening the robustness of our model.…”

Section: A Feature-level Augmentationmentioning

confidence: 99%

“…At present, the issue of data noise and incompleteness have attracted much attention in traffic forecasting study. Actually, urban networks are inevitably exposed to various incidents such as traffic accidents, sensor malfunctions and large-scale power outrages, leading to the presence of missing data [19], [20]. In this context, methods integrating self-supervised learning, especially contrastive learning, are gradually rising to prominence.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Graph Contrastive Learning for Urban Traffic Flow Forecasting

Pan,

Ren,

2023

Preprint

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<p>Graph Neural Networks(GNNs) integrating contrastive learning have attracted growing attentions in urban traffic flow forecasting. However, most existing graph contrastive learning works donot perform well in capturing local-global spatial dependencies and designing contrastive learning scheme in both spatial and temporal dimensions. We argue that these works cannot well extract the spatial-temporal features and are easily affected by data noise. In light of these challenges, this paper proposes an innovative Urban Spatial-Temporal Graph Contrastive Learning framework(UrbanGCL) to improve the accuracy of urban traffic flow forecasting. Specifically, UrbanGCL proposes feature-level and topology-level data augmentation to solve data noise and incompleteness, learn both local and global topology features. Afterwards, the augmented traffic feature matrices and adjacency matrices are fed into a simple yet effective dual-branch network with shared parameters to capture spatialtemporal correlations within traffic sequences. Moreover, we propose the spatial and temporal contrastive learning auxiliary tasks, so as to alleviate the sparsity of supervision signal, and extract the most critical spatial-temporal information. Extensive experimental results on four real-world urban datasets demonstrate that UrbanGCL significantly outperforms other state-ofthe-art methods. The source code of our model is available at https://github.com/panlinnn/UrbanGCL.</p>

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Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection

Cited by 5 publications

References 20 publications

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Traffic flow prediction based on graph wave adaptive spatiotemporal graph convolution network

Spatial-Temporal Graph Contrastive Learning for Urban Traffic Flow Forecasting

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