.png){kind=icon}
返回
Contrastive calibration on consensus and complementary multi-view representations
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2026.113291.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
• 提出了一种基于自表示NMF的多视图表示学习模型。
• 结合模型级融合和晚期融合以实现全面的信息集成。
• 通过联合分解和分离分解统一了共识信息和互补信息。
• 通过对比校准正则化增强了表示的判别性。
• 在多样化的多视图数据集上展示了优于当前最优方法的优势。
Keyword:
NMF-based multi-view learning
consensus and complementary information
joint and disjoint factorizations
contrastive calibration regularization
multi-view representation discrimination
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Multi-view clustering via multi-manifold regularized non-negative matrix factorization.png)
NEURAL NETWORKS
OAAI
NEURAL NETWORKSIF6.3
OAAI分享收藏Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints
PREAI
分享收藏Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation基于非冗余正则化的非负矩阵分解和流形学习的多视图数据表示INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Label-consistent kernel transform learning-based sparse hashing for cross-modal retrieval基于标签一致的核变换学习稀疏哈希的跨模态检索
PREAI
分享收藏Enhancing link prediction through adversarial training in deep Nonnegative Matrix Factorization
PREAI
分享收藏
查看更多内容