Block Randomized Optimization for Adaptive Hypergraph Learning
The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big matrices fast. Here, block randomized Singular Value Decomposition (SVD) via subspace iteration is integrated within adaptive hypergraph weight estimation for image tagging, as a first approach. Specifically, creating low-rank submatrices along the main diagonal by tessellation permits fast matrix inversions via randomized SVD. Moreover, a second approach is proposed for solving the linear system in the optimization problem of hypergraph learning by employing the conjugate gradient method. Both proposed approaches achieve high accuracy in image tagging measured by F1 score and succeed to reduce the computational requirements of adaptive hypergraph weight estimation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Community detection in the sparse hypergraph stochastic block model
We consider the community detection problem in sparse random hypergraphs. Angelini et al. (2015) conjectured the existence of a sharp threshold on model parameters for community detection in sparse hypergraphs generated …
Community DetectionStochastic Block ModelConcomitant Group Testing
In this paper, we introduce a variation of the group testing problem capturing the idea that a positive test requires a combination of multiple ``types'' of item. Specifically, we assume that there are multiple disjoint …
Hypergraph Optimization for Multi-structural Geometric Model Fitting
Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between d…
ClusteringmodelAn Efficient Multilinear Optimization Framework for Hypergraph Matching
Hypergraph matching has recently become a popular approach for solving correspondence problems in computer vision as it allows to integrate higher-order geometric information. Hypergraph matching can be formulated as a t…
Hypergraph MatchingDual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement
Inspired by the success of graph contrastive learning, researchers have begun exploring the benefits of contrastive learning over hypergraphs. However, these works have the following limitations in modeling the high-orde…
Contrastive LearningHypergraph Contrastive LearningHypergraph representationsRepresentation Learning