Journal ArticleDOI
Low-Rank Embedding for Robust Image Feature Extraction
TLDR
A robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm’s robustness in image feature extraction.Abstract:
Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm’s robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui .read more
Citations
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Journal ArticleDOI
Dimensionality Reduction With Enhanced Hybrid-Graph Discriminant Learning for Hyperspectral Image Classification
TL;DR: In this article, the authors proposed a hybrid-graph learning method to reveal the complex high-order relationships of the hyperspectral image (HSI), termed enhanced hybrid graph discriminant learning (EHGDL).
Journal ArticleDOI
Low-Rank Preserving Projection Via Graph Regularized Reconstruction
TL;DR: A novel method named low-rank preserving projection via graph regularized reconstruction (LRPP_GRR) is proposed, which imposes the graph constraint on the reconstruction error of data instead of introducing the extra regularization term to capture the local structure of data, which can greatly reduce the complexity of the model.
Journal ArticleDOI
Dual Shared-Specific Multiview Subspace Clustering
TL;DR: A novel dual shared-specific multiview subspace clustering (DSS-MSC) approach that simultaneously learns the correlations between shared information across multiple views and also utilizes view-specific information to depict specific property for each independent view is presented.
Journal ArticleDOI
Feature Selective Projection with Low-Rank Embedding and Dual Laplacian Regularization
Chang Tang,Xinwang Liu,Xinzhong Zhu,Jian Xiong,Miaomiao Li,Jingyuan Xia,Xiangke Wang,Lizhe Wang +7 more
TL;DR: This paper designs an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise.
Journal ArticleDOI
Nonpeaked Discriminant Analysis for Data Representation
TL;DR: The authors present a nonpeaked discriminant analysis (NPDA) technique, in which cutting L1-norm is adopted as the distance metric, and an efficient iterative algorithm is designed for the optimization of the proposed objective.
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