Subspace Clustering

By René Vidal. IEEE Signal Processing Magazine, 2011

299 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/14b455a3fde8bb134e73bb5fe99d1ffbeb4d75c8

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. Cheng Deng
2. Feiping Nie
3. 刘晗
4. Yi Yang
5. Heng Huang
6. Xiaotong Zhang
7. Xianchao Zhang
8. Xu Yang

Citations in the corpus

• Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (2020, background): “In recent years, subspace clustering [33] has aroused widespread research interests in unsupervised learning and is successfully exploited to various applications, such as image segmentation [22, 36], motion segmentation [14, 4], image clustering [40, 5, 37, 38, 11], genes expression microarray clus”
• Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “In BiCo, the eigenvectors of features in the v-th view of the t-th task U (v)t can be thought of as dimensionality reduction with a linear combination of associated features, which can also be referred to as subspace basis [Vidal, 2011].”
• Multiview Consensus Graph Clustering (2019, methodology): “Since most multiview learning methods need to exploit the similarity, k-means clustering [23], non-negative matrix factorization (NMF) [24], SC [3], graph embedding [25], subspace learning [26], and multiple-kernel learning [27] are usually extended to multiview setting.”
• Subspace clustering (2012, background): “75 Such problems common to all of these areas are (1) how to treat diversity of clustering solutions (are diverse clustering solutions to be unified or to be presented individually?); (2) how to effectively summarize and present diversity to a user of clustering algorithms; (3) how to treat redundan”
• Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017, background): “el at a given iteration). An open question is how can robust and dynamic robust PCA ideas be successfully adapted to solve other more general related problems. One such problem is subspace clustering [82] which involves clustering a given dataset into one of K different low-dimensional subspaces. Th”