Proceedings Article10.1109/ICCV.2015.482
Multi-view Subspace Clustering
Hongchang Gao,Feiping Nie,Xuelong Li,Heng Huang +3 more
- 07 Dec 2015
- pp 4238-4246
TL;DR: A novel multi-view subspace clustering method that performs clustering on the subspace representation of each view simultaneously and proposes to use a common cluster structure to guarantee the consistence among different views.
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Abstract: For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.
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Citations
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TL;DR: A new unsupervised DR method called sparsity preserving projections (SPP), which aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a L1 regularization-related objective function.
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GMC: Graph-Based Multi-View Clustering
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Generalized Latent Multi-View Subspace Clustering
TL;DR: This work proposes a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC), which explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation.
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Latent Multi-view Subspace Clustering
Changqing Zhang,Qinghua Hu,Huazhu Fu,Pengfei Zhu,Xiaochun Cao +4 more
- 21 Jul 2017
TL;DR: A novel Latent Multi-view Subspace Clustering method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views, which makes subspace representation more accurate and robust as well.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Kamran Ghasedi Dizaji,Amirhossein Herandi,Cheng Deng,Weidong Cai,Heng Huang +4 more
- 01 Oct 2017
TL;DR: A new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments is proposed, which indicates the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
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