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Journal articles on the topic 'Multi-dimensional graph signal processing'

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Author: Grafiati

Published: 14 March 2023

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1

Zheng, Xianwei, Yuan Yan Tang, Jiantao Zhou, Jianjia Pan, Shouzhi Yang, Youfa Li, and Patrick S. P. Wang. "Multi-Level Downsampling of Graph Signals via Improved Maximum Spanning Trees." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 03 (February 19, 2019): 1958005. http://dx.doi.org/10.1142/s0218001419580059.

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Graph signal processing (GSP) is an emerging field in the signal processing community. Novel GSP-based transforms, such as graph Fourier transform and graph wavelet filter banks, have been successfully utilized in image processing and pattern recognition. As a rapidly developing research area, graph signal processing aims to extend classical signal processing techniques to signals with irregular underlying structures. One of the hot topics in GSP is to develop multi-scale transforms such that novel GSP-based techniques can be applied in image processing or other related areas. For designing graph signal multi-scale frameworks, downsampling operations that ensuring multi-level downsampling should be specifically constructed. Among the existing downsampling methods in graph signal processing, the state-of-the-art method was constructed based on the maximum spanning tree (MST). However, when using this method for multi-level downsampling of graph signals defined on unweighted densely connected graphs, such as social network data, the sampling rates are not close to [Formula: see text]. This phenomenon is summarized as a new problem and called downsampling unbalance problem in this paper. Due to the unbalance, MST-based downsampling method cannot be applied to construct graph signal multi-scale transforms. In this paper, we propose a novel and efficient method to detect and reduce the downsampling unbalance generated by the MST-based method. For any given graph signal, we apply the graph density to construct a measurement of the downsampling unbalance generated by the MST-based method. If a graph signal has large unbalance possibility, the multi-level downsampling is conducted after the MST is improved. The experimental results on synthetic and real-world social network data show that downsampling unbalance can be efficiently detected and then reduced by our method.

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Liao, Kefei, Zerui Yu, Ningbo Xie, and Junzheng Jiang. "Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing." Remote Sensing 14, no. 5 (February 24, 2022): 1110. http://dx.doi.org/10.3390/rs14051110.

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Target position estimation is one of the important research directions in array signal processing. In recent years, the research of target azimuth estimation based on graph signal processing (GSP) has sprung up, which provides new ideas for the Direction of Arrival (DoA) application. In this article, by extending GSP-based DOA to joint azimuth and distance estimation and constructing a fully connected graph signal model, a multi-target joint azimuth and distance estimation method based on GSP is proposed. Firstly, the fully connection graph model is established related to the phase information of a linear array. For the fully connection graph, the Fourier transform method is used to solve the estimated response function, and the one-dimensional estimation of azimuth and distance is completed, respectively. Finally, the azimuth and distance estimation information are combined, and the false points in the merging process are removed by using CLEAN algorithm to complete the two-dimensional estimation of targets. The simulation results show that the proposed method has a smaller mean square error than the Multiple Signal Classification (MUSIC) algorithm in azimuth estimation under the condition of a low signal-to-noise ratio and more accurate response values than the MUSIC algorithm in distance estimation under any signal-to-noise ratio in multi-target estimation.

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Yankelevsky, Yael, and Michael Elad. "Finding GEMS: Multi-Scale Dictionaries For High-Dimensional Graph Signals." IEEE Transactions on Signal Processing 67, no. 7 (April 1, 2019): 1889–901. http://dx.doi.org/10.1109/tsp.2019.2899822.

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Jian, Xingchao, Feng Ji, and Wee Peng Tay. "Generalizing Graph Signal Processing: High Dimensional Spaces, Models and Structures." Foundations and Trends® in Signal Processing 17, no. 3 (2023): 209–90. http://dx.doi.org/10.1561/2000000119.

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5

Xiong, Chao, Wen Li, Yun Liu, and Minghui Wang. "Multi-Dimensional Edge Features Graph Neural Network on Few-Shot Image Classification." IEEE Signal Processing Letters 28 (2021): 573–77. http://dx.doi.org/10.1109/lsp.2021.3061978.

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Mathur, Priyanka, and Vijay Kumar Chakka. "Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals." IEEE Sensors Journal 22, no. 8 (April 15, 2022): 7971–78. http://dx.doi.org/10.1109/jsen.2022.3156152.

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Park, Han-Mu, and Kuk-Jin Yoon. "Exploiting multi-layer graph factorization for multi-attributed graph matching." Pattern Recognition Letters 127 (November 2019): 85–93. http://dx.doi.org/10.1016/j.patrec.2018.09.024.

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8

Rakhimberdina, Zarina, Xin Liu, and Tsuyoshi Murata. "Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder." Sensors 20, no. 21 (October 22, 2020): 6001. http://dx.doi.org/10.3390/s20216001.

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With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population’s structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

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Li, Shuang, Bing Liu, and Chen Zhang. "Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4920670.

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Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.

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Oselio, Brandon, Alex Kulesza, and Alfred O. Hero. "Multi-Layer Graph Analysis for Dynamic Social Networks." IEEE Journal of Selected Topics in Signal Processing 8, no. 4 (August 2014): 514–23. http://dx.doi.org/10.1109/jstsp.2014.2328312.

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Lézoray, Olivier. "Hierarchical morphological graph signal multi-layer decomposition for editing applications." IET Image Processing 14, no. 8 (June 19, 2020): 1549–60. http://dx.doi.org/10.1049/iet-ipr.2019.0576.

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12

Li, Yuzhong, Wenming Tang, and Guixiong Liu. "HPEFT for Hierarchical Heterogeneous Multi-DAG in a Multigroup Scan UPA System." Electronics 8, no. 5 (May 5, 2019): 498. http://dx.doi.org/10.3390/electronics8050498.

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Multidirected acyclic graph (DAG) workflow scheduling is a key problem in the heterogeneous distributed environment in the distributed computing field. A hierarchical heterogeneous multi-DAG workflow problem (HHMDP) was proposed based on the different signal processing workflows produced by different grouping and scanning modes and their hierarchical processing in specific functional signal processing modules in a multigroup scan ultrasonic phased array (UPA) system. A heterogeneous predecessor earliest finish time (HPEFT) algorithm with predecessor pointer adjustment was proposed based on the improved heterogeneous earliest finish time (HEFT) algorithm. The experimental results denote that HPEFT reduces the makespan, ratio of the idle time slot (RITS), and missed deadline rate (MDR) by 3.87–57.68%, 0–6.53%, and 13–58%, respectively, and increases relative relaxation with respect to the deadline (RLD) by 2.27–8.58%, improving the frame rate and resource utilization and reducing the probability of exceeding the real-time period. The multigroup UPA instrument architecture in multi-DAG signal processing flow was also provided. By simulating and verifying the scheduling algorithm, the architecture and the HPEFT algorithm is proved to coordinate the order of each group of signal processing tasks for improving the instrument performance.

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13

Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.

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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.

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Mehta, Sumet, Bi-Sheng Zhan, and Xiang-Jun Shen. "Weighted Neighborhood Preserving Ensemble Embedding." Electronics 8, no. 2 (February 16, 2019): 219. http://dx.doi.org/10.3390/electronics8020219.

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Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.

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15

Slota, George M., Cameron Root, Karen Devine, Kamesh Madduri, and Sivasankaran Rajamanickam. "Scalable, Multi-Constraint, Complex-Objective Graph Partitioning." IEEE Transactions on Parallel and Distributed Systems 31, no. 12 (December 1, 2020): 2789–801. http://dx.doi.org/10.1109/tpds.2020.3002150.

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16

Huang, Yanquan, Haoliang Yuan, and Loi Lei Lai. "Latent multi-view semi-supervised classification by using graph learning." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 05 (June 20, 2020): 2050039. http://dx.doi.org/10.1142/s0219691320500393.

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Multi-view learning is a hot research direction in the field of machine learning and pattern recognition, which is attracting more and more attention recently. In the real world, the available data commonly include a small number of labeled samples and a large number of unlabeled samples. In this paper, we propose a latent multi-view semi-supervised classification method by using graph learning. This work recovers a latent intact representation to utilize the complementary information of the multi-view data. In addition, an adaptive graph learning technique is adopted to explore the local structure of this latent intact representation. To fully use this latent intact representation to discover the label information of the unlabeled data, we consider to unify the procedures of computing the latent intact representation and the labels of unlabeled data as a whole. An alternating optimization algorithm is designed to effectively solve the optimization of the proposed method. Extensive experimental results demonstrate the effectiveness of our proposed method.

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17

Liu, Zhi, Jixin Bian, Deju Zhang, Yang Chen, Guojiang Shen, and Xiangjie Kong. "Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting." Electronics 11, no. 16 (August 21, 2022): 2620. http://dx.doi.org/10.3390/electronics11162620.

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Accurate urban travel demand forecasting can help organize traffic flow, improve traffic utilization, reduce passenger waiting time, etc. It plays an important role in intelligent transportation systems. Most of the existing research methods construct static graphs from a single perspective or two perspectives, without considering the dynamic impact of time changes and various factors on traffic demand. Moreover, travel demand is also affected by regional functions such as weather, etc. To address these issues, we propose an urban travel demand prediction framework based on dynamic multi-view coupled graph convolution (DMV-GCN). Specifically, we dynamically construct demand similarity graphs based on node features to model the dynamic correlation of demand. Then we combine it with the predefined geographic similarity graph, functional similarity graph, and road similarity graph. We use coupled graph convolution network and gated recurrent units (GRU), to model the spatio-temporal correlation in traffic. We conduct extensive experiments over two large real-world datasets. The results verify the superior performance of our proposed approach for the urban travel demand forecasting task.

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18

Tugnait, Jitendra K. "Sparse-Group Lasso for Graph Learning From Multi-Attribute Data." IEEE Transactions on Signal Processing 69 (2021): 1771–86. http://dx.doi.org/10.1109/tsp.2021.3057699.

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19

Tugnait, Jitendra K. "Deviance Tests for Graph Estimation From Multi-Attribute Gaussian Data." IEEE Transactions on Signal Processing 68 (2020): 5632–47. http://dx.doi.org/10.1109/tsp.2020.3023575.

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20

Ioannidis, Vassilis N., Antonio G. Marques, and Georgios B. Giannakis. "Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning." IEEE Transactions on Signal Processing 68 (2020): 6535–46. http://dx.doi.org/10.1109/tsp.2020.3028495.

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21

Rahimi, Sahere, Ali Aghagolzadeh, and Mehdi Ezoji. "Human action recognition based on the Grassmann multi-graph embedding." Signal, Image and Video Processing 13, no. 2 (September 6, 2018): 271–79. http://dx.doi.org/10.1007/s11760-018-1354-1.

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22

Zhang, Dongxiao, Pierre-Marc Jodoin, Cuihua Li, Yundong Wu, and Guorong Cai. "Novel Graph Cuts Method for Multi-Frame Super-Resolution." IEEE Signal Processing Letters 22, no. 12 (December 2015): 2279–83. http://dx.doi.org/10.1109/lsp.2015.2477079.

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23

Edwards, Michael, Xianghua Xie, Robert I. Palmer, Gary K. L. Tam, Rob Alcock, and Carl Roobottom. "Graph convolutional neural network for multi-scale feature learning." Computer Vision and Image Understanding 194 (May 2020): 102881. http://dx.doi.org/10.1016/j.cviu.2019.102881.

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24

Zhang, Jingwei, Zhongdao Wang, Yali Li, and Shengjin Wang. "Node-Adaptive Multi-Graph Fusion Using Extreme Value Theory." IEEE Signal Processing Letters 27 (2020): 351–55. http://dx.doi.org/10.1109/lsp.2020.2970811.

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25

Li, Guodong, Xvan Qin, He Liu, Kaiyuan Jiang, and Aili Wang. "Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means." Electronics 11, no. 20 (October 13, 2022): 3298. http://dx.doi.org/10.3390/electronics11203298.

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Automatic modulation recognition (AMR) has been wildly used in both military and civilian fields. Since the recognition of digital signal at low signal-to-noise (SNR) ratio is difficult and complex, in this paper, a clustering analysis algorithm is proposed for its recognition. Firstly, the digital signal constellation is extracted from the received waveform (digital signal + noise) by using the orthogonal decomposition and then, it is denoised by using an algorithm referred to as auto density-based spatial clustering technique in noise (ADBSCAN). The combination of density peak clustering (DPC) algorithm and improved K-means clustering is used to extract the constellation’s graph features, the eigenvalues are input into cascade support vector machine (SVM) multi-classifiers, and the signal modulation mode is obtained. BPSK, QPSK, 8PSK, 16QAM and 32QAM five kinds of digital signals are trained and classified by our proposed method. Compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR (less than 4dB), which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

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Li, Han, Xinyu Wang, Zhongguo Yang, Sikandar Ali, Ning Tong, and Samad Baseer. "Correlation-Based Anomaly Detection Method for Multi-sensor System." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–13. http://dx.doi.org/10.1155/2022/4756480.

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In industry, sensor-based monitoring of equipment or environment has become a necessity. Instead of using a single sensor, multi-sensor system is used to fully detect abnormalities in complex scenarios. Recently, physical models, signal processing technology, and various machine learning models have improved the performance. However, these methods either do not consider the potential correlation between features or do not take advantage of the sequential changes of correlation while constructing an anomaly detection model. This paper firstly analyzes the correlation characteristic of a multi-sensor system, which shows a lot of clues to the anomaly/fault propagation. Then, a multi-sensor anomaly detection method, which finds and uses the correlation between features contained in the multidimensional time-series data, is proposed. The method converts the multidimensional time-series data into temporal correlation graphs according to time window. By transforming time-series data into graph structure, the task of anomaly detection is considered as a graph classification problem. Moreover, based on the stability and dynamics of the correlation between features, a structure-sensitive graph neural network is used to establish the anomaly detection model, which is used to discover anomalies from multi-sensor system. Experiments on three real-world industrial multi-sensor systems with anomalies indicate that the method obtained better performance than baseline methods, with the mean value of F1 score reaching more than 0.90 and the mean value of AUC score reaching more than 0.95. That is, the method can effectively detect anomalies of multidimensional time series.

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Akbarian, Behnaz, and Abbas Erfanian. "A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network." Biomedical Signal Processing and Control 59 (May 2020): 101878. http://dx.doi.org/10.1016/j.bspc.2020.101878.

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Xia, Wei, Junbin Chen, and Lisha Yu. "Distributed Adaptive Multi-Task Learning Based on Partially Observed Graph Signals." IEEE Transactions on Signal and Information Processing over Networks 7 (2021): 522–38. http://dx.doi.org/10.1109/tsipn.2021.3101109.

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Li, Juan-Hui, Chang-Dong Wang, Pei-Zhen Li, and Jian-Huang Lai. "Discriminative metric learning for multi-view graph partitioning." Pattern Recognition 75 (March 2018): 199–213. http://dx.doi.org/10.1016/j.patcog.2017.06.012.

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Wu, Jiaxin, Sheng-hua Zhong, and Yan Liu. "Dynamic graph convolutional network for multi-video summarization." Pattern Recognition 107 (November 2020): 107382. http://dx.doi.org/10.1016/j.patcog.2020.107382.

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Gutiérrez-Gómez, Leonardo, Alexandre Bovet, and Jean-Charles Delvenne. "Multi-Scale Anomaly Detection on Attributed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 678–85. http://dx.doi.org/10.1609/aaai.v34i01.5409.

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Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes. Finding anomalies in these systems is crucial for detecting abuses such as credit card frauds, web spams or network intrusions. Intuitively, anomalous nodes are defined as nodes whose attributes differ starkly from the attributes of a certain set of nodes of reference, called the context of the anomaly. While some methods have proposed to spot anomalies locally, globally or within a community context, the problem remain challenging due to the multi-scale composition of real networks and the heterogeneity of node metadata. Here, we propose a principled way to uncover outlier nodes simultaneously with the context with respect to which they are anomalous, at all relevant scales of the network. We characterize anomalous nodes in terms of the concentration retained for each node after smoothing specific signals localized on the vertices of the graph. Besides, we introduce a graph signal processing formulation of the Markov stability framework used in community detection, in order to find the context of anomalies. The performance of our method is assessed on synthetic and real-world attributed networks and shows superior results concerning state of the art algorithms. Finally, we show the scalability of our approach in large networks employing Chebychev polynomial approximations.

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32

Khachan, Mohammed, Patrick Chenin, and Hafsa Deddi. "Polyhedral Representation and Adjacency Graph in n-dimensional Digital Images." Computer Vision and Image Understanding 79, no. 3 (September 2000): 428–41. http://dx.doi.org/10.1006/cviu.2000.0859.

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Tugnait, Jitendra. "Corrections to “Sparse-Group Lasso for Graph Learning From Multi-Attribute Data”." IEEE Transactions on Signal Processing 69 (2021): 4758. http://dx.doi.org/10.1109/tsp.2021.3104727.

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Kadambari, Sai Kiran, and Sundeep Prabhakar Chepuri. "Product Graph Learning From Multi-Domain Data With Sparsity and Rank Constraints." IEEE Transactions on Signal Processing 69 (2021): 5665–80. http://dx.doi.org/10.1109/tsp.2021.3115947.

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Liu, Guohua, and Jianchun Duan. "RGB-D image segmentation using superpixel and multi-feature fusion graph theory." Signal, Image and Video Processing 14, no. 6 (February 17, 2020): 1171–79. http://dx.doi.org/10.1007/s11760-020-01647-x.

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GU, JIANPING, LI ZHANG, and CUN CHENG. "DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (November 2013): 1350051. http://dx.doi.org/10.1142/s0219691313500513.

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A new algorithm named dynamic graph merging (DGM) for automatic image segmentation is explored. Firstly a novel variational model for multi-section cut is introduced by decomposing the traditional cut into two parts, the harmonic cut and the elastic energy of the boundary. The new energy is called the continuous combined cut. Secondly a new algorithm that removes those edges with higher energy and synchronously merges their starting and ending vertices in an ordered manner is proposed. The continual merging process would iteratively contract the graph, merge those homogeneous vertices into bigger and bigger super-pixels, and fuse the remainder edges into longer and longer boundaries. So we call this algorithm dynamic graph merging. Merging criterions based on the continuous combined cut model are also discussed, which will be used to determine whether a given edge should collapse. Since the merging condition should be highly related to the image content, we present different predicates for structure images and texture images respectively. This algorithm whose efficiency is showed by experiments has a linear time/space complexity, and can efficiently segment gray/color and 2D/3D images.

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Sun, Ning, Ling Leng, Jixin Liu, and Guang Han. "Multi-stream slowFast graph convolutional networks for skeleton-based action recognition." Image and Vision Computing 109 (May 2021): 104141. http://dx.doi.org/10.1016/j.imavis.2021.104141.

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Cao, Pingping, Pengpeng Chen, and Qiang Niu. "Multi-label image recognition with two-stream dynamic graph convolution networks." Image and Vision Computing 113 (September 2021): 104238. http://dx.doi.org/10.1016/j.imavis.2021.104238.

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Wan, Jianwu, Liang Niu, Bing Bai, and Hongyuan Wang. "Graph Regularized Deep Discrete Hashing for Multi-Label Image Retrieval." IEEE Signal Processing Letters 27 (2020): 1994–98. http://dx.doi.org/10.1109/lsp.2020.3034538.

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Huang, Shudong, Zhao Kang, Ivor W. Tsang, and Zenglin Xu. "Auto-weighted multi-view clustering via kernelized graph learning." Pattern Recognition 88 (April 2019): 174–84. http://dx.doi.org/10.1016/j.patcog.2018.11.007.

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Ye, Xulun, and Jieyu Zhao. "Multi-manifold clustering: A graph-constrained deep nonparametric method." Pattern Recognition 93 (September 2019): 215–27. http://dx.doi.org/10.1016/j.patcog.2019.04.029.

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Gu, Xianbin, and Jeremiah D. Deng. "A multi-feature bipartite graph ensemble for image segmentation." Pattern Recognition Letters 131 (March 2020): 98–104. http://dx.doi.org/10.1016/j.patrec.2019.12.017.

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Saboksayr, Seyed Saman, Gonzalo Mateos, and Mujdat Cetin. "Online discriminative graph learning from multi-class smooth signals." Signal Processing 186 (September 2021): 108101. http://dx.doi.org/10.1016/j.sigpro.2021.108101.

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Frishman, Yaniv, and Ayellet Tal. "Multi-Level Graph Layout on the GPU." IEEE Transactions on Visualization and Computer Graphics 13, no. 6 (November 2007): 1310–19. http://dx.doi.org/10.1109/tvcg.2007.70580.

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Li, Chaoyue, Lian Zou, Cien Fan, Hao Jiang, and Yifeng Liu. "Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition." Electronics 10, no. 18 (September 8, 2021): 2198. http://dx.doi.org/10.3390/electronics10182198.

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Graph convolutional networks (GCNs), which model human actions as a series of spatial-temporal graphs, have recently achieved superior performance in skeleton-based action recognition. However, the existing methods mostly use the physical connections of joints to construct a spatial graph, resulting in limited topological information of the human skeleton. In addition, the action features in the time domain have not been fully explored. To better extract spatial-temporal features, we propose a multi-stage attention-enhanced sparse graph convolutional network (MS-ASGCN) for skeleton-based action recognition. To capture more abundant joint dependencies, we propose a new strategy for constructing skeleton graphs. This simulates bidirectional information flows between neighboring joints and pays greater attention to the information transmission between sparse joints. In addition, a part attention mechanism is proposed to learn the weight of each part and enhance the part-level feature learning. We introduce multiple streams of different stages and merge them in specific layers of the network to further improve the performance of the model. Our model is finally verified on two large-scale datasets, namely NTU-RGB+D and Skeleton-Kinetics. Experiments demonstrate that the proposed MS-ASGCN outperformed the previous state-of-the-art methods on both datasets.

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Carrillo, Rafael E., Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel, and Pierre-Jean Alet. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution." Energies 13, no. 21 (November 3, 2020): 5763. http://dx.doi.org/10.3390/en13215763.

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Abstract:

Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps. Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model. It also introduces a data-driven clear-sky production estimation for normalization. The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data. The results demonstrate the performance and robustness of the approach: with gaps of four hours on average in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.

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Deng, Cheng, Rongrong Ji, Dacheng Tao, Xinbo Gao, and Xuelong Li. "Weakly Supervised Multi-Graph Learning for Robust Image Reranking." IEEE Transactions on Multimedia 16, no. 3 (April 2014): 785–95. http://dx.doi.org/10.1109/tmm.2014.2298841.

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Hu, Lingyue, Kailong Zhao, Bingo Wing-Kuen Ling, and Yuxin Lin. "Activity recognition via correlation coefficients based graph with nodes updated by multi-aggregator approach." Biomedical Signal Processing and Control 79 (January 2023): 104255. http://dx.doi.org/10.1016/j.bspc.2022.104255.

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Yu, Tianhang, Minjian Zhao, Jie Zhong, Jian Zhang, and Pei Xiao. "Low‐complexity graph‐based turbo equalisation for single‐carrier and multi‐carrier FTN signalling." IET Signal Processing 11, no. 7 (September 2017): 838–45. http://dx.doi.org/10.1049/iet-spr.2016.0251.

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Cheng, Dawei, Fangzhou Yang, Sheng Xiang, and Jin Liu. "Financial time series forecasting with multi-modality graph neural network." Pattern Recognition 121 (January 2022): 108218. http://dx.doi.org/10.1016/j.patcog.2021.108218.

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