Diversity-induced Multi-view Subspace Clustering

By Xiaochun Cao, Changqing Zhang, Huan Fu, Si Liu, Hua Zhang. Computer Vision and Pattern Recognition, 2015

690 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bce793c02546a213a71ad8fca4b73e645e9b32d6

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-view clustering: A survey (2018, positive): “Moreover, Cao et al.[122] put forward a constrained multi-view video face clustering method, which considers both the video face pairwise constraints and the multiview consistence simultaneously.”
Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, positive): “For instance, the traditional subspace clustering was extended by [6], where the authors presented a diversity-induced mechanism for multi-view subspace clustering.”
Latent Multi-view Subspace Clustering (2017, methodology): “Therefore, the multi-view subspace clustering methods have been proposed [3, 31, 9], in which each data point is described with information from multiple sources of features.”
Multi-View Clustering via Deep Matrix Factorization (2017, methodology): “, coefficient matrix Z in NaMSC and DiMSC (Cao et al. 2015)).”
Essential Tensor Learning for Multi-View Spectral Clustering (2018, methodology): “DiMSC [5] employs the HSIC as a diversity term to explore the complementarity of multi-view representations.”
Consistent and Specific Multi-View Subspace Clustering (2018, methodology): “DiMSC (Cao et al. 2015) is on behalf of diversity-induced multi-view subspace clustering which explores complementary information across different views by enforcing the diversity.”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “Competitors: We compare the proposed method with seven representative clustering algorithms: the standard spect ral lustering algorithm with the most informative view (SPC best), LRR algorithm with the most informative view (LRR best), robust multiview spectral clustering via low-rank and sparse dec”
Low-Rank Tensor Constrained Multiview Subspace Clustering (2015, methodology): “To exploit complementary information, some methods employ co-regularization and co-training strategies , e.g., [4], [5], [23], [37] and [22].”
Graph Learning for Multiview Clustering (2018, neutral): “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].”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “, [15], [16]), the data should not have low-dimensional views.”
Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview (2021, neutral): “[50] considered both the consistency and the diversity among the multiple views; Gao et al.”
Scalable Multi-view Subspace Clustering with Unified Anchors (2021, background): “[4] propose to induce Hilbert-Schmidt Independence Criterion (HSIC) to explore diverse subspace representations.”
COMIC: Multi-view Clustering Without Parameter Selection (2019, background): “…(Chaudhuri et al., 2009), multi-view matrix factorization (Zhang et al., 2018), multi-view subspace clustering/spectral clustering (Kumar et al., 2011; Cao et al., 2015; Lu et al., 2016; Zhang et al., 2017), and deep multi-view clustering (Andrew et al., 2013; Wang et al., 2015; Zhao et al., 2017).”
LSDT: Latent Sparse Domain Transfer Learning for Visual Adaptation (2016, background): “Compared with LRR, SSC can be scalable [43] and multi-view [45], and well supported by both theoretical analysis [37] and experimental results [3] in handling the data points near the intersections of subspaces.”
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, background): “[31] propose to induce Hilbert-Schmidt Independence Criterion (HSIC) to explore diverse subspace representations.”
Large-scale Multi-view Subspace Clustering in Linear Time (2019, methodology): “Basically, for multi-view data (Gao et al. 2015; Cao et al. 2015; Kang et al. 2019c) (2) With different forms of f , Eq.”