Relevant bibliographies by topics / High-dimensional sparse graph

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'High-dimensional sparse graph'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "High-dimensional sparse graph"

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Zou, Yuanhang, Zhihao Ding, Jieming Shi, Shuting Guo, Chunchen Su, and Yafei Zhang. "EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3543–56. http://dx.doi.org/10.14778/3611540.3611546.

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In modern online services, it is of growing importance to process web-scale graph data and high-dimensional sparse data together into embeddings for downstream tasks, such as recommendation, advertisement, prediction, and classification. There exist learning methods and systems for either high-dimensional sparse data or graphs, but not both. There is an urgent need in industry to have a system to efficiently process both types of data for higher business value, which however, is challenging. The data in Tencent contains billions of samples with sparse features in very high dimensions, and grap
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Xie, Anze, Anders Carlsson, Jason Mohoney, et al. "Demo of marius." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2759–62. http://dx.doi.org/10.14778/3476311.3476338.

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Graph embeddings have emerged as the de facto representation for modern machine learning over graph data structures. The goal of graph embedding models is to convert high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces that preserve the graph structure properties. However, learning a graph embedding model is a resource intensive process, and existing solutions rely on expensive distributed computation to scale training to instances that do not fit in GPU memory. This demonstration showcases Marius: a new open-source engine for learning graph embedding models
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Liu, Jianyu, Guan Yu, and Yufeng Liu. "Graph-based sparse linear discriminant analysis for high-dimensional classification." Journal of Multivariate Analysis 171 (May 2019): 250–69. http://dx.doi.org/10.1016/j.jmva.2018.12.007.

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Wang, Li-e., and Xianxian Li. "A Clustering-Based Bipartite Graph Privacy-Preserving Approach for Sharing High-Dimensional Data." International Journal of Software Engineering and Knowledge Engineering 24, no. 07 (2014): 1091–111. http://dx.doi.org/10.1142/s0218194014500363.

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Driven by mutual benefits, there is a demand for transactional data sharing among organizations or parties for research or business analysis purpose. It becomes an essential concern to provide privacy-preserving data sharing and meanwhile maintain data utility, due to the fact that transactional data may contain sensitive personal information. Existing privacy-preserving methods, such as k-anonymity and l-diversity, cannot handle high-dimensional sparse data well, since they would bring about much data distortion in the anonymization process. In this paper, we use bipartite graphs with node at
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Ni, Li, Peng Manman, and Wu Qiang. "A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks." Applied Sciences 14, no. 11 (2024): 4946. http://dx.doi.org/10.3390/app14114946.

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With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named SEG_SC.This algorithm introduces a new spectral clustering model that explores the underlying struct
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Saul, Lawrence K. "A tractable latent variable model for nonlinear dimensionality reduction." Proceedings of the National Academy of Sciences 117, no. 27 (2020): 15403–8. http://dx.doi.org/10.1073/pnas.1916012117.

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We propose a latent variable model to discover faithful low-dimensional representations of high-dimensional data. The model computes a low-dimensional embedding that aims to preserve neighborhood relationships encoded by a sparse graph. The model both leverages and extends current leading approaches to this problem. Like t-distributed Stochastic Neighborhood Embedding, the model can produce two- and three-dimensional embeddings for visualization, but it can also learn higher-dimensional embeddings for other uses. Like LargeVis and Uniform Manifold Approximation and Projection, the model produc
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Li, Xinyu, Xiaoguang Gao, and Chenfeng Wang. "A Novel BN Learning Algorithm Based on Block Learning Strategy." Sensors 20, no. 21 (2020): 6357. http://dx.doi.org/10.3390/s20216357.

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Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated b
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Dobson, Andrew, and Kostas Bekris. "Improved Heuristic Search for Sparse Motion Planning Data Structures." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (2021): 196–97. http://dx.doi.org/10.1609/socs.v5i1.18334.

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Sampling-based methods provide efficient, flexible solutions for motion planning, even for complex, high-dimensional systems. Asymptotically optimal planners ensure convergence to the optimal solution, but produce dense structures. This work shows how to extend sparse methods achieving asymptotic near-optimality using multiple-goal heuristic search during graph constuction. The resulting method produces identical output to the existing Incremental Roadmap Spanner approach but in an order of magnitude less time.
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Li, Peng, Mosharaf Md Parvej, Chenghao Zhang, Shufang Guo, and Jing Zhang. "Advances in the Development of Representation Learning and Its Innovations against COVID-19." COVID 3, no. 9 (2023): 1389–415. http://dx.doi.org/10.3390/covid3090096.

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In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-di
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Li, Ying, Xiaojun Xu, and Jianbo Li. "High-Dimensional Sparse Graph Estimation by Integrating DTW-D Into Bayesian Gaussian Graphical Models." IEEE Access 6 (2018): 34279–87. http://dx.doi.org/10.1109/access.2018.2849213.

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Dissertations / Theses on the topic "High-dimensional sparse graph"

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ARTARIA, ANDREA. "Objective Bayesian Analysis for Differential Gaussian Directed Acyclic Graphs." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/55327.

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Often we are confronted with heterogeneous multivariate data, i.e., data coming from several categories, and the interest may center on the differential structure of stochastic dependence among the variables between the groups. The focus in this work is on the two groups problem and is faced modeling the system through a Gaussian directed acyclic graph (DAG) couple linked in a fashion to obtain a joint estimation in order to exploit, whenever they exist, similarities between the graphs. The model can be viewed as a set of separate regressions and the proposal consists in assigning a non-local
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Xu, Ning. "Accurate variable selection and causal structure recovery in high-dimensional data." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/22920.

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From the perspective of econometrics, an accurate variable selection method greatly enhances the reliability of causal analysis and interpretation of the estimators, espe- cially in a world of ever-expanding data dimensions. While variable selection methods in machine learning and statistics have been developed rapidly and applied widely in different branches of data science in the last decade, they have been more slowly adopted in econometrics. Nevertheless, the machine learning methods, including lasso, forward regression, cross-validation and marginal correlation ranking (also called vari-
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Jalali, Ali 1982. "Dirty statistical models." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5088.

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In fields across science and engineering, we are increasingly faced with problems where the number of variables or features we need to estimate is much larger than the number of observations. Under such high-dimensional scaling, for any hope of statistically consistent estimation, it becomes vital to leverage any potential structure in the problem such as sparsity, low-rank structure or block sparsity. However, data may deviate significantly from any one such statistical model. The motivation of this thesis is: can we simultaneously leverage more than one such statistical structural model, to
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Book chapters on the topic "High-dimensional sparse graph"

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Skillicorn, David B. "Representation by Graphs." In Understanding High-Dimensional Spaces. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33398-9_6.

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O’ Mahony, Niall, Anshul Awasthi, Joseph Walsh, and Daniel Riordan. "Latent Space Cartography for Geometrically Enriched Latent Spaces." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_38.

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AbstractThere have been many developments in recent years on the exploitation of non-Euclidean geometry for the better representation of the relation between subgroups in datasets. Great progress has been made in this field of Disentangled Representation Learning, in leveraging information geometry divergence, manifold regularisation and geodesics to allow complex dynamics to be captured in the latent space of the representations produced. However, interpreting the high-dimensional latent spaces of the modern deep learning-based models involved is non-trivial. Therefore, in this paper, we inve
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Mateus, Diana, Christian Wachinger, Selen Atasoy, Loren Schwarz, and Nassir Navab. "Learning Manifolds." In Machine Learning in Computer-Aided Diagnosis. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0059-1.ch018.

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Computer aided diagnosis is often confronted with processing and analyzing high dimensional data. One alternative to deal with such data is dimensionality reduction. This chapter focuses on manifold learning methods to create low dimensional data representations adapted to a given application. From pairwise non-linear relations between neighboring data-points, manifold learning algorithms first approximate the low dimensional manifold where data lives with a graph; then, they find a non-linear map to embed this graph into a low dimensional space. Since the explicit pairwise relations and the n
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Oulhaj, Ziyad, Yoshiyuki Ishii, Kento Ohga, et al. "Deep Mapper: Efficient Visualization of Plausible Conformational Pathways." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240803.

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Acquiring plausible pathways on high-dimensional structural distributions is beneficial in several domains. For example, in the drug discovery field, a protein conformational pathway, i.e. a highly probable sequence of protein structural changes, is useful to analyze interactions between the protein and the ligands, helping to create new drugs. Recently, a state-of-the-art method in drug discovery was presented, which efficiently computes protein pathways using latent variables obtained from an isometric auto-encoding of the space of 3D density maps associated to protein conformations. However
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Ding, Zhibang, Pengbo Zhao, Shuangjie Liang, and Xinmeng Wang. "A Knowledge Graph-Based Approach to Anti-Smuggling Intelligence Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241381.

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Customs anti-smuggling intelligence as an important link in the anti-smuggling chain. From the massive case information in time to find effective intelligence clues, all kinds of anti-smuggling case data for automated intelligence extraction, high efficiency according to the case of time, location and other elements of the case thread; anti-smuggling case intelligence under the environment of big data for intelligent correlation, anti-smuggling cases in the intelligence of intelligent research and analysis. This is especially important for the construction of China’s public security informatiz
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Conference papers on the topic "High-dimensional sparse graph"

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Tugnait, Jitendra K. "On Sparse High-Dimensional Graph Estimation from Multi-Attribute Data." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942781.

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Wu, Di, Gang Lu, and Zhicheng Xu. "Robust and Accurate Representation Learning for High-dimensional and Sparse Matrices in Recommender Systems." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00075.

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Zhang, Jiaqi, Meng Wang, Qinchi Li, Sen Wang, Xiaojun Chang, and Beilun Wang. "Quadratic Sparse Gaussian Graphical Model Estimation Method for Massive Variables." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/410.

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We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized thre
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Rivera, Grecia C. G., Juan G. Colonna, and Marcelo Ruiz. "Discovery of Conditionally Independent Networks Among Gene Expressions in Breast Cancer Using Fast Step Graph." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.7499.

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The heterogeneity of the causes of breast cancer and these complex gene interactions that characterize this neoplasm present significant challenges to understanding and treating the disease. This study is motivated by the need to identify interconnected networks of breast cancer genes, specifically those that represent conditional independence relationships. To construct these networks, we propose the use of the Fast Step Graph algorithm, which belongs to the family of sparse, high-dimensional Gaussian Graphical Models, applied to the PAM50 gene expression dataset. This dataset was stratified
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Ilinca, Florin, Jean-François Hétu, Martin Audet, and Randall Bramley. "Simulation of 3-D Mold-Filling and Solidification Processes on Distributed Memory Parallel Architectures." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0805.

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Abstract This work presents industrial mold-filling applications of a three-dimensional stabilized finite element solver on distributed memory parallel architectures. The paper focuses on the solution algorithm and parallel implementation for complex multiphasics problems involving high Reynolds number flows with free surfaces, turbulence modeling and heat transfer. Standard domain decomposition methods (Chaco, Metis) are applied to the graph of nodes obtained from the finite element mesh, and a distributed-memory MPI programming model is used. An implicit time integration scheme and a segrega
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Lee, Yong Hoon, R. E. Corman, Randy H. Ewoldt, and James T. Allison. "A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67541.

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A novel multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) framework is proposed to utilize a minimal number of training samples efficiently for sequential model updates. All the sample points are enforced to be feasible, and to provide coverage of sparsely explored sparse design regions using a new optimization subproblem. The MO-ASMO method only evaluates high-fidelity functions at feasible sample points. During an exploitation sample phase, samples are selected to enhance solution accuracy rather than the global exploration. Sampling tasks are especially challenging for
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Misra, Siddharth, and Aditya Chakravarty. "Fracture Monitoring and Characterization Using Unsupervised Microseismic Data Analysis." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24412-ms.

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Abstract The study shows the use of unsupervised manifold learning on microseismic data for fracture monitoring and characterization. Manifold learning condenses complex, high-dimensional data into more concise, lower-dimensional representations that encapsulate valuable underlying patterns and structures of the data. The study leverages Uniform Manifold Approximation and Projection (UMAP) methodology to efficiently convert high-dimensional data into a graph-based, lower-dimensional representation. This transformative approach adeptly captures meaningful patterns and structures while preservin
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Morris, Clinton, and Carolyn C. Seepersad. "Identification of High Performance Regions of High-Dimensional Design Spaces With Materials Design Applications." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67769.

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Design exploration methods seek to identify sets of candidate designs or regions of the design space that yield desirable performance. Commonly, the dimensionality of the design space exceeds the limited dimensions supported by standard graphical techniques, making it difficult for human designers to visualize or understand the underlying structure of the design space. With standard visualization tools, it is sometimes challenging to visualize a multi-dimensional Pareto frontier, but it is even more difficult to visualize the collections of design (input) variable values that yield those Paret
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Hu, Binbin, Zhengwei Wu, Jun Zhou, et al. "MERIT: Learning Multi-level Representations on Temporal Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/288.

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Recently, representation learning on temporal graphs has drawn increasing attention, which aims at learning temporal patterns to characterize the evolving nature of dynamic graphs in real-world applications. Despite effectiveness, these methods commonly ignore the individual- and combinatorial-level patterns derived from different types of interactions (e.g.,user-item), which are at the heart of the representation learning on temporal graphs. To fill this gap, we propose MERIT, a novel multi-level graph attention network for inductive representation learning on temporal graphs.We adaptively em
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Zhu, Xiaofeng, Cong Lei, Hao Yu, Yonggang Li, Jiangzhang Gan, and Shichao Zhang. "Robust Graph Dimensionality Reduction." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/452.

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In this paper, we propose conducting Robust Graph Dimensionality Reduction (RGDR) by learning a transformation matrix to map original high-dimensional data into their low-dimensional intrinsic space without the influence of outliers. To do this, we propose simultaneously 1) adaptively learning three variables, \ie a reverse graph embedding of original data, a transformation matrix, and a graph matrix preserving the local similarity of original data in their low-dimensional intrinsic space; and 2) employing robust estimators to avoid outliers involving the processes of optimizing these three ma
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