Latent Multi-view Subspace Clustering

By Changqing Zhang, Qinghua Hu, Huan Fu, Peng Fei Zhu, Xiaochun Cao. Computer Vision and Pattern Recognition, 2017

485 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c2ed59f38cf8a3f7ae608a63eb88f74482232905

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Huan Fu
3. Xiaochun Cao
4. Changqing Zhang
5. Zhouchen Lin
6. Jianlong Wu
7. Yuan Xie
8. Qinghua Hu

In Media Technology

1. Xiaochun Cao
2. Changqing Zhang
3. Huan Fu
4. Yuan Xie
5. Joey Tianyi Zhou
6. Dacheng Tao
7. En Zhu
8. Peng Hu

Citations in the corpus

• GMC: Graph-Based Multi-View Clustering (2020, unknown): “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 multiview subspace clustering [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43].”
• Consistent and Specific Multi-View Subspace Clustering (2018, background): “Multi-view latent representation (Zhang et al. 2017) is based on the assumption that each view is originated from one underlying latent representation.”
• Robust Multi-View Clustering With Incomplete Information (2022, methodology): “We compare SURE with 11 multi-view clustering baselines including CCA [12], KCCA [11], DCCA [1], DCCAE [2], LMSC [52], MvC-DMF [53], SwMC [54], BMVC [14], AE 2 - Nets [4], PVC [8], and MvCLN [10].”
• Generalized Latent Multi-View Subspace Clustering (2020, methodology): “More importantly, and expanding on the linear correlation used in our previous work [20], we further generalize our model for non-linear correlation, and accordingly propose generalized Latent Multi-view Subspace Clustering (gLMSC).”
• Essential Tensor Learning for Multi-View Spectral Clustering (2018, background): “clustering with generalized tensor nuclear norm. Then [29] adopts the t-SVD based tensor nuclear norm for better representation, and [30] proposes the tensorial t-product representation. Zhang et al. [31] jointly learns the underlying latent representation of features and the multi-view low-rank rep”
• Deep Partial Multi-View Learning (2020, background): “Multi-view clustering algorithms [17], [18], [19], [20], [21], [22], [23] usually search for consistent clustering hypotheses across different views, where the representative methods include co-regularization based [17], co-training based [18] and high-order multi-view clustering [19].”
• Large-scale Multi-view Subspace Clustering in Linear Time (2019, background): “(Zhang et al. 2017).”
• Graph Learning for Multiview Clustering (2018, unknown): “Multiview subspace clustering methods also need post-process k-means clustering algorithms because they finally use SC methods to obtain the cluster indicators [29]–[32].”
• Scalable Multi-view Subspace Clustering with Unified Anchors (2021, background): “[33, 35] reconstruct the data points in a latent space which could make subspace representation more accurate and robust.”