Co-regularized Multi-view Spectral Clustering

By Abhishek Kumar, Piyush Rai, Hal Daumé Iii. 2011

1,307 citations recorded by the source corpus.

How researchers see this paper

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

1. Dacheng Tao
2. Yi Yang
3. Feiping Nie
4. Xiangnan He
5. Luc Van Gool
6. Changqing Zhang
7. Zhe Gan
8. Xuelong Li

Citations in the corpus

• Audio-Visual Instance Discrimination with Cross-Modal Agreement (2020, background): “Classical approaches can be broadly categorized in co-training procedures [6, 7, 30, 43, 50, 67, 84] that maximize the mutual agreement between views, multiple kernel learning procedures [5, 40, 44] which use kernels to model different views, and subspace learning procedures [15, 68] which seek to f”
• Latent Multi-view Subspace Clustering (2017, methodology): “SPCBestSV is the standard spectral clustering with the best single view.”
• Click-through-based cross-view learning for image search (2014, background): “advanced co-training for data clustering and designed effective algorithms for multi-view data [16].”
• GMC: Graph-Based Multi-View Clustering (2020, methodology): “Our work is also related to multi-view spectral clustering [18], [19], [20], [21], [22], [23], [24], [25].”
• Comment-based multi-view clustering of web 2.0 items (2014, background): “CoSC [24].”
• Co-regularized Alignment for Unsupervised Domain Adaptation (2018, methodology): “The related ideas of co-training [3] and co-regularization [37, 36] have been successfully used for semi-supervised learning as well as unsupervised learning [21, 20].”
• Essential Tensor Learning for Multi-View Spectral Clustering (2018, methodology): “[4] aims to find the complementary information across views based on a co-regularization method.”
• Multi-Task Clustering with Model Relation Learning (2018, background): “Co-regularization [Kumar et al. , 2011; Minh et al. , 2016] takes the disagreement between the classification or clustering re-sults of any two views as a regularization term in the objective function.”
• Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “, KU=UUT as the similarity measure metric [19].”
• Graph Learning for Multiview Clustering (2018, unknown): “Kumar et al. [18], [19] proposed unsupervised multiview clustering methods based on co-training learning, such as cotraining multiview spectral clustering (SC) and co-regularized multiview SC (CRSC).”
• Multi-View Clustering and Feature Learning via Structured Sparsity (2013, methodology): “…against two most recent multi-view clustering methods: the Multi-Modal Spectral Clustering (MMSC) method (Cai et al., 2011) and Co-regularized Multi-view Spectral Clustering (CMSC) method (Kumar et al., 2011), which have demonstrated state-of-the-art clustering performance on multi-view data.”
• Multi-View Intact Space Learning (2015, unknown): “Many variants [8], [9], [10], [11], [12], [13], [14], [15], such as co-EM [8] and co-regularization [10], [11], have since been developed.”
• Multi-view Subspace Clustering (2015, methodology): “The co-regularized multi-view spectral clustering was introduced in [13] to 2015 IEEE International Conference on Computer Vision”
• Attributed Network Embedding for Learning in a Dynamic Environment (2017, background): “Œe problem of aŠributed network embedding is also related to but distinct from multi-modality or multi-view embedding [23, 49, 53].”