Low-rank representation-based regularized subspace learning method for unsupervised domain adaptation

2019 ◽  
Vol 79 (3-4) ◽  
pp. 3031-3047
Author(s):  
Liran Yang ◽  
Min Men ◽  
Yiming Xue ◽  
Ping Zhong
Keyword(s):  
Domain Adaptation ◽  
Subspace Learning ◽  
Low Rank ◽  
Learning Method ◽  
Unsupervised Domain Adaptation ◽  
Low Rank Representation

Discriminative Low-Rank Subspace Learning with Nonconvex Penalty

2019 ◽  
Vol 33 (10) ◽  
pp. 1951006
Author(s):  
Kan Xie ◽  
Wei Liu ◽  
Yue Lai ◽  
Weijun Li
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Subspace Learning ◽  
Experimental Results ◽  
Low Rank ◽  
Learning Method ◽  
Original Solution ◽  
Discriminative Subspace ◽  
Low Rank Representation ◽  
Facial Images

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

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

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

Robust subspace learning-based low-rank representation for manifold clustering

2018 ◽  
Vol 31 (11) ◽  
pp. 7921-7933 ◽  
Author(s):  
Kewei Tang ◽  
Zhixun Su ◽  
Wei Jiang ◽  
Jie Zhang ◽  
Xiyan Sun ◽  
...  
Keyword(s):  
Subspace Learning ◽  
Low Rank ◽  
Low Rank Representation ◽  
Manifold Clustering

scholarly journals Active Sampling for Open-Set Classification without Initial Annotation

2019 ◽  
Vol 33 ◽  
pp. 4416-4423
Author(s):  
Zhao-Yang Liu ◽  
Sheng-Jun Huang
Keyword(s):  
Performance Measures ◽  
Low Rank ◽  
Structured Sparsity ◽  
Learning Method ◽  
Active Sampling ◽  
Open Set ◽  
Multiple Datasets ◽  
Multi Stage ◽  
Active Learning Method ◽  
Low Rank Representation

Open-set classification is a common problem in many real world tasks, where data is collected for known classes, and some novel classes occur at the test stage. In this paper, we focus on a more challenging case where the data examples collected for known classes are all unlabeled. Due to the high cost of label annotation, it is rather important to train a model with least labeled data for both accurate classification on known classes and effective detection of novel classes. Firstly, we propose an active learning method by incorporating structured sparsity with diversity to select representative examples for annotation. Then a latent low-rank representation is employed to simultaneously perform classification and novel class detection. Also, the method along with a fast optimization solution is extended to a multi-stage scenario, where classes occur and disappear in batches at each stage. Experimental results on multiple datasets validate the superiority of the proposed method with regard to different performance measures.

Guide Subspace Learning for Unsupervised Domain Adaptation

2020 ◽  
Vol 31 (9) ◽  
pp. 3374-3388 ◽  
Author(s):  
Lei Zhang ◽  
Jingru Fu ◽  
Shanshan Wang ◽  
David Zhang ◽  
Zhaoyang Dong ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Subspace Learning ◽  
Unsupervised Domain Adaptation
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