Robust Orthonormal Subspace Learning (ROSL) for Efficient Low-Rank Recovery

2016 ◽  
pp. 12-1-12-14
Keyword(s):  
Subspace Learning ◽  
Low Rank

Subspace Learning Based Low-Rank Representation

2017 ◽  
pp. 416-431 ◽  
Author(s):  
Kewei Tang ◽  
Xiaodong Liu ◽  
Zhixun Su ◽  
Wei Jiang ◽  
Jiangxin Dong
Keyword(s):  
Subspace Learning ◽  
Low Rank ◽  
Low Rank Representation

scholarly journals Similarity Learning via Kernel Preserving Embedding

2019 ◽  
Vol 33 ◽  
pp. 4057-4064 ◽  
Author(s):  
Zhao Kang ◽  
Yiwei Lu ◽  
Yuanzhang Su ◽  
Changsheng Li ◽  
Zenglin Xu
Keyword(s):  
Fundamental Problem ◽  
Dimensional Space ◽  
Similarity Measures ◽  
Original Data ◽  
Subspace Learning ◽  
Reconstruction Error ◽  
Semisupervised Learning ◽  
Low Rank ◽  
Similarity Learning ◽  
Similarity Information

Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semisupervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-ofthe-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.

Joint low-rank representation and spectral regression for robust subspace learning

2020 ◽  
Vol 195 ◽  
pp. 105723
Author(s):  
Yong Peng ◽  
Leijie Zhang ◽  
Wanzeng Kong ◽  
Feiwei Qin ◽  
Jianhai Zhang
Keyword(s):  
Subspace Learning ◽  
Low Rank ◽  
Low Rank Representation ◽  
Spectral Regression

Generalized Transfer Subspace Learning Through Low-Rank Constraint

2014 ◽  
Vol 109 (1-2) ◽  
pp. 74-93 ◽  
Author(s):  
Ming Shao ◽  
Dmitry Kit ◽  
Yun Fu
Keyword(s):  
Subspace Learning ◽  
Low Rank ◽  
Rank Constraint
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