Adaptive hypergraph learning with multi-stage optimizations for image and tag recommendation

Karantaidis, Georgios; Sarridis, Ioannis; Kotropoulos, Constantine

doi:10.1016/j.image.2021.116367

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Let f and H be fixed,

\boldsymbol{J}=\boldsymbol{I}-\frac{\boldsymbol{A}}{1+\theta }

{\boldsymbol{f}}^{\ast }

is obtained through Equation ():

{\boldsymbol{f}}^{\ast }=\underset{\boldsymbol{f}}{\arg \kern0.5em \min }T\left(\boldsymbol{f};\boldsymbol{w},\boldsymbol{H}\right)=\omega {\boldsymbol{J}}^{-1}\boldsymbol{y}

where

\omega =\frac{\theta }{1+\theta }

. Update

{\boldsymbol{w}}^{\ast }

. The optimal

{\boldsymbol{w}}^{\ast }

is obtained through the least mean square method 29 . It is sequentially revised upward along the negative gradient of the optimization function.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment

Fu,

Shan,

Liu

et al. 2024

Int J Imaging Syst Tech

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Brain functional network (BFN) has emerged as a practical path to explore biomarkers for early mild cognitive impairment (eMCI). Currently, most of BFNs only considered the topology structure between two brain regions and ignored the high‐order information among multiple brain regions. We proposed an adaptive manifold regularization method to construct a new BFN. Firstly, a traditional hypergraph was constructed through a low‐order BFN. Then, an adaptive hypergraph was obtained by updating the traditional hypergraph weight and structure through adaptive hypergraph learning. An adaptive hypergraph manifold regularization term was constructed by the Laplacian matrix of the adaptive hypergraph. Finally, the low‐order BFN was optimized through the adaptive hypergraph manifold regularization and sparse regularization. The experimental results confirmed that the proposed method outperformed other state‐of‐the‐art methods in classification performance and stability. This study revealed the causes of changes in topological properties and provided a reference for the clinical diagnosis of eMCI.

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“…Let f and H be fixed,

\boldsymbol{J}=\boldsymbol{I}-\frac{\boldsymbol{A}}{1+\theta }

{\boldsymbol{f}}^{\ast }

is obtained through Equation ():

{\boldsymbol{f}}^{\ast }=\underset{\boldsymbol{f}}{\arg \kern0.5em \min }T\left(\boldsymbol{f};\boldsymbol{w},\boldsymbol{H}\right)=\omega {\boldsymbol{J}}^{-1}\boldsymbol{y}

where

\omega =\frac{\theta }{1+\theta }

. Update

{\boldsymbol{w}}^{\ast }

. The optimal

{\boldsymbol{w}}^{\ast }

is obtained through the least mean square method 29 . It is sequentially revised upward along the negative gradient of the optimization function.…”

Section: Methodsmentioning

confidence: 99%

“…The optimal w Ã is obtained through the least mean square method. 29 It is sequentially revised upward along the negative gradient of the optimization function. w Ã is computed by…”

Section: Adaptive Hypergraph Learningmentioning

confidence: 99%

Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment

Fu,

Shan,

Liu

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…In the application of hypergraph learning to recommendation tasks, Georgios Karantaidis et al [52] proposed an optimization method spanning multiple stages using hypergraphs. By the optimization of hypergraph ranking, hypergraph updates, and adaptive edge weight, accurate ranking vectors generated for image and label recommendation.…”

Section: Hypergraph Learningmentioning

confidence: 99%

A Recommendation Method Based on Multi-Source Heterogeneous Hypergraphs and Contrastive Learning

Wan,

Ding

2024

IEEE Access

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Fusion of multi-source information is one of the primary methods to alleviate data sparsity in recommender systems. Hypergraphs have shown remarkable capabilities in dealing with the diversity of multi-source information, especially in modeling high-order user-item relationships. However, most hypergraph research focuses on constructing hypergraphs using a particular type of hyperedge, which might not fully capture the high-order implicit associations among heterogeneous information sources. Furthermore, existing Hypergraph Attention Networks (HyperGAT), mainly emphasizes on information propagation between nodes and hyperedges, insufficiently exploring density information within complex hyperedges. Moreover, when hypergraphs combine data from multiple heterogeneous graphs, redundant information from different viewpoints can reduce the effectiveness of multivariate modeling hyperedges. Thus, we propose a recommendation algorithm based on multi-source heterogeneous hypergraphs and contrastive learning (MHCLR), which improves recommendation accuracy through multi-source information fusion and higherorder information correlation. First, multi-source heterogeneous hypergraphs (MHC) are generated by combining distance, behavior, attribute, and prediction hyperedges. We mine associations among multisource information, enhancing high-order semantic connections among users and items. Then, a Spatial Density Hypergraph Attention Network (SD-HGAT) is proposed based on composite hyperedges, which enriches the user and item embedding representations by focusing on nodes and hyperedges density. Finally, we design a multiple cross view comparative learning (MCC) that compares views centered around knowledge graphs with hypergraphs, improving the accuracy of multivariate relationship modeling and enabling multi-level user profiling construction. It is observed that MHCLR outperforms the baselines in terms of recall, precision, and NDCG based on the experimental results by Yelp, Last-FM, and Douban.

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HGAT-BR: Hyperedge-based graph attention network for basket recommendation

et al. 2022

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Adaptive hypergraph learning with multi-stage optimizations for image and tag recommendation

Cited by 8 publications

References 27 publications

Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment

Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment

A Recommendation Method Based on Multi-Source Heterogeneous Hypergraphs and Contrastive Learning

HGAT-BR: Hyperedge-based graph attention network for basket recommendation

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