Results 21 to 30 of about 9,987 (195)
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Remote Sensing, 2021 Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
doaj +1 more source
Deep Hypergraph Structure Learning
, 2022 Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure.
Zhang, Zizhao +3 more
openaire +2 more sources
Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine
Applied Sciences, 2021 Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on ...
Zhewei Liu +4 more
doaj +1 more source
Learning Low Degree Hypergraphs
, 2022 We study the problem of learning a hypergraph via edge detecting queries. In this problem, a learner queries subsets of vertices of a hidden hypergraph and observes whether these subsets contain an edge or not. In general, learning a hypergraph with $m$ edges of maximum size $d$ requires $Ω((2m/d)^{d/2})$ queries.
Balkanski, Eric +2 more
openaire +2 more sources
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
Ma, Jing +5 more
openaire +2 more sources
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
doaj +1 more source
Learning and Testing Variable Partitions [PDF]
, 2020 $ $Let $F$ be a multivariate function from a product set $\Sigma^n$ to an Abelian group $G$. A $k$-partition of $F$ with cost $\delta$ is a partition of the set of variables $\mathbf{V}$ into $k$ non-empty subsets $(\mathbf{X}_1, \dots, \mathbf{X}_k ...
Bogdanov, Andrej, Wang, Baoxiang
core +2 more sources
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
doaj +1 more source
Optimal Query Complexity for Reconstructing Hypergraphs [PDF]
, 2010 In this paper we consider the problem of reconstructing a hidden weighted hypergraph of constant rank using additive queries. We prove the following: Let $G$ be a weighted hidden hypergraph of constant rank with n vertices and $m$ hyperedges. For any $m$
Bshouty, Nader H., Mazzawi, Hanna
core +6 more sources
Learning a Hidden Hypergraph [PDF]
, 2005 We consider the problem of learning a hypergraph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden hypergraph or not. We show that an r-uniform hypergraph with m edges and n vertices is learnable with O(2$^{\rm 4{\it r}}$m · poly(r,log n)) queries with high probability.
Dana Angluin, Jiang Chen
openaire +1 more source