Kernel-Based Weighted Multi-view Clustering

By Grigorios Tzortzis, Aristidis C. Likas. 2012

255 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Liu
2. Dacheng Tao
3. Hao Wang
4. Changqing Zhang
5. Huan Fu
6. Yan Yang
7. Lei Zhang
8. Shiliang Sun

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xiaochun Cao
3. Huan Fu
4. Changqing Zhang
5. Yuan Xie
6. Lei Zhang
7. Qinghua Hu
8. Ming-Hsuan Yang

Citations in the corpus

• Generalized Latent Multi-View Subspace Clustering (2020, unknown): “Based on MKL, [33] further proposes to automatically weight different views.”
• A Survey on Multi-View Clustering (2017, unknown): “To make a full use of all views, the weighted combination K = ∑m v=1 w p vK , wv ≥ 0, ∑m v=1 wv = 1, p ≥ 1 will be used in kernel k-means (24) and spectral clustering (8) to obtain the corresponding multiview kernel k-means and multi-view spectral clustering [41].”
• Diversity-induced Multi-view Subspace Clustering (2015, background): “Nevertheless, the main limitation of the existing methods [8, 9, 12, 16, 17, 24, 25] is that they could not guarantee the complementarity across different similarity matrices corresponding to different views.”
• On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, background): “The aim is to learn the weighted combination of these kernel and the partitioni ng simultaneously [7].”
• Multi-view clustering: A survey (2018, unknown): “[51], where the weights to the kernels were assigned according to the information quality of the corresponding views.”
• Multi-view learning overview: Recent progress and new challenges (2017, background): “Corel Images Dataset7 Corel images dataset [124] consists of 34 categories, each with 100 images.”
• Latent Multi-view Subspace Clustering (2017, methodology): “Furthermore, the researchers in [25] proposed a more general way based on MKL to learn the weights of different kernels.”
• GMC: Graph-Based Multi-View Clustering (2020, methodology): “The other related multi-view clustering methods can be roughly classified into three categories: co-training style clustering [19], [26], [27], [28], multi-kernel clustering [29], [30], [31], [32], and multi-view subspace clustering [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43].”
• Low-Rank Tensor Constrained Multiview Subspace Clustering (2015, background): “Instead of equally adding these kernels, views are expressed in terms of given kernel matrices and a weighted combination of these kernels is learned in parallel to the partitioning in [17].”