Low-Rank Tensor Constrained Multiview Subspace Clustering

By Changqing Zhang, Huan Fu, Si Liu, Guangcan Liu, Xiaochun Cao. IEEE International Conference on Computer Vision, 2015

487 citations recorded by the source corpus.

Subfields: Computational Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b7f6d111049dd324b6396032e4e154c85cdc502c

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In Computer Vision and Pattern Recognition

Citations in the corpus

Consistent and Specific Multi-View Subspace Clustering (2018, methodology): “Following (Zhang et al. 2015), for face datasets, we resize the images to 48 × 48 and extract three types of features: View1 intensity (4,096 dimensions), View2 LBP (Ojala, Pietikainen, and Maenpaa 2002) (3,304 dimensions) and View3 Gabor (Lades et al.”
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance (2021, methodology): “Some recent methods accelerate the time complexity into O(n(3)) per iteration [3], [15], [24]–[29].”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “The corresponding phenomena appear several times, for example, DiMSC on Scene-15, (RMSC, DiMSC, and LTMSC) on MITIndoor-67, and (RMSC and DiMSC) on Caltech-101, where their performances are worse than directly using spectral clustering with single best feature.”
Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (2020, methodology): “In the past few years, many multiview clustering methods have been proposed, such as the multiview k-means clustering [17], canonical correlation analysis (CCA)-based method [18], co-regularized multiview spectral clustering [19], low-rank tensor-based method [20], the deep matrix factorization-base”
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017, methodology): “Low rank structure of tensor data has been successfully utilized in applications including missing data imputation [Liu et al., 2013, Zhao et al., 2015], multilinear robust principal component analysis [Inoue et al., 2009], and subspace clustering [Zhang et al., 2015a].”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “Low-Rank Tensor Constrained Multi-view Subspace Clustering (LT-MSC) [16] explores the high-order correlation among these subspace representations.”
Multiview Consensus Graph Clustering (2019, methodology): “[37]–[41] are used to learn a better similarity matrix than using a fixed function.”
Essential Tensor Learning for Multi-View Spectral Clustering (2018, methodology): “For example, [23] extends the LRR into multi-view subspace clustering with generalized tensor nuclear norm.”
Multi-view learning overview: Recent progress and new challenges (2017, background): “[21] proposed low-rank tensor constrained multi-view subspace clustering which regards the subspace representation matrices of different views as a tensor and the tensor is equipped with a low-rank constraint.”
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.”
Graph Learning for Multiview Clustering (2018, unknown): “There are 1440 images of 20 object categories [30].”
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].”
Scalable Multi-view Subspace Clustering with Unified Anchors (2021, background): “Many multiview clustering algorithms have been proposed in existing literature, among which multi-view subspace clustering is quite popular [18, 21, 25, 34, 39].”
Multi-omic and multi-view clustering algorithms: review and cancer benchmark (2018, background): “clustering Factorization based on low-rank tensors (63) Matlab”