In multi-view clustering, datasets are comprised of different representations of the data, or views. Although each view could individually be used, exploiting information from all views together could improve the cluster quality. In this paper a new model Multi-View Kernel Spectral Clustering (MV...
多视图核谱聚类算法(Multi-view Kernel Spectral Clustering, MVKSC)是一种用于处理具有多个不同视图或表示的数据集的机器学习方法。 这种算法利用了核技巧和谱聚类理论,旨在从多个不同的角度或特征集合中提取数据的内在结构,以提高聚类的准确性和稳定性。以下是MVKSC算法的详细介绍,包括其关键步骤和相关公式。 MVKSC...
Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding Inf. Fusion (2020) KangZ. et al. Low-rank kernel learning for graph-based clustering Knowl.-Based Syst. (2019) ZhangR. et al. Feature selection with multi-view data: A survey Inf. Fusion (2019) Zhang...
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Consen- sus graph and spectral representation for one-step multi-view kernel based clustering. Knowledge-Based Systems, page 108250, 2022. 2 [11] Ruiyi Fang, Liangjian Wen, Zhao Kang, and Jianzhuang Liu. Structure-preserving graph representation learning. IEE...
Hierarchical multiple kernel clustering. In Proceedings of the AAAI conference on artificial intelli- gence, volume 35, pages 8671–8679, 2021. [19] Jiyuan Liu, Xinwang Liu, Yuexiang Yang, Xifeng Guo, Marius Kloft, and Liangzhong He. Multiview subs...
鲁棒且低秩的多核聚类模型(Robust Low-rank Kernel Multi-view Clustering, RLKMSC)是一种专门设计用于处理多视图数据的复杂性和异质性的聚类算法。 RLKMSC结合了多核学习、低秩近似和鲁棒性优化,以在存在噪声和异常值的情况下找到数据的固有结构。 下面是RLKMSC算法的详细解释,包括关键步骤和相关的数学公式。
classical multi-view clustering methods such as multi-view kernel k-means clustering are point-based methods. The performance of point-based methods will be fairly good when the data points are distributed around the center point. The plane-based clustering methods can handle data points that are...
Multiple kernel k-means with incomplete kernelsMKKM-IK-MK2019IEEE TPAMIcode Efficient and effective incomplete multi-view clusteringEE-IMVC2019AAAI Efficient and effective regularized incomplete multi-view clusteringEE-R-IMVC2020IEEE TPAMIcode
Spectral clustering is a helpful technique for clustering non-convex data, which extends the clustering to data with multiple partial views. Still, it has a higher running time thanks to cubic time complexity, while extending spectral embeddings to unseen samples is non-trivial and prevents model ...