Results 21 to 30 of about 1,673 (179)
Edge Representation Learning with Hypergraphs
CoRR, 2021 NeurIPS ...
Jaehyeong Jo +5 more
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Learning Causal Effects on Hypergraphs
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we focus on the problem of individual
Jing Ma 0002 +5 more
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Review on graph learning for dimensionality reduction of hyperspectral image
Geo-spatial Information Science, 2020 Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction
Liangpei Zhang, Fulin Luo
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The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
CoRR, 2013 Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hypergraphs via graphs or on tensor methods which are only applicable under special conditions.
M. Hein +3 more
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Hypergraph-Supervised Deep Subspace Clustering
Mathematics, 2021 Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays
Yu Hu, Hongmin Cai
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Hypergraph Learning with Hyperedge Expansion [PDF]
, 2012 We propose a new formulation called hyperedge expansion (HE) for hypergraph learning. The HE expansion transforms the hypergraph into a directed graph on the hyperedge level. Compared to the existing works (e.g. star expansion or normalized hypergraph cut), the learning results with HE expansion would be less sensitive to the vertex distribution among ...
Pu, Li, Faltings, Boi
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Multiview Hypergraph Fusion Network for Change Detection in High-Resolution Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCurrently, convolutional neural networks and transformers have been the dominant paradigms for change detection (CD) thanks to their powerful local and global feature extraction capabilities. However, with the improvement of resolution, spatial, spectral,
Xue Zhao +5 more
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Attribute-enhanced metric learning for face retrieval
EURASIP Journal on Image and Video Processing, 2018 Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval.
Yuchun Fang, Qiulong Yuan
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Adaptive Learning a Hidden Hypergraph
CoRR, 2016 Learning a hidden hypergraph is a natural generalization of the classical group testing problem that consists in detecting unknown hypergraph $H_{un}=H(V,E)$ by carrying out edge-detecting tests. In the given paper we focus our attention only on a specific family $\mathcal{F}(t,s,\ell)$ of localized hypergraphs for which the total number of vertices ...
Arkadii G. D'yachkov +3 more
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An Ensemble Hypergraph Learning Framework for Recommendation
, 2021 Recommender systems are designed to predict user preferences over collections of items. These systems process users’ previous interactions to decide which items should be ranked higher to satisfy their desires. An ensemble recommender system can achieve great recommendation performance by effectively combining the decisions generated by individual ...
Gharahighehi, Alireza +2 more
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