Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction
Meng Pang; Binghui Wang; Yiu-Ming Cheung; Chuang Lin
刊名IEEE Access
2017
文献子类期刊论文
英文摘要Most off-the-shelf subspace learning methods directly calculate the statistical characteristics of the original input images, while ignoring different contributions of different image components. In fact, to extract efficient features for image analysis, the noise or trivial structure in images should have little contribution and the intrinsic structure should be uncovered. Motivated by this observation, we propose a new subspace learning method, namely, discriminant manifold learning via sparse coding (DML_SC) for robust feature extraction. Specifically, we first decompose each input image into several components via dictionary learning, and then regroup the components into a more important part (MIP) and a less important part (LIP). The MIP can be considered as the clean portion of the image residing on a low-dimensional submanifold, while the LIP as noise or trivial structure within the image. Finally, the MIP and LIP are incorporated into manifold learning to learn a desired discriminative subspace. The proposed method is general for both cases with and without class labels, hence generating supervised DML_SC (SDML_SC) and unsupervised DML_SC (UDML_SC). Experimental results on four benchmark data sets demonstrate the efficacy of the proposed DML_SCs on both image recognition and clustering tasks.
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语种英语
WOS记录号WOS:000431432800001
内容类型期刊论文
源URL[http://ir.siat.ac.cn:8080/handle/172644/11950]  
专题深圳先进技术研究院_医工所
作者单位IEEE Access
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GB/T 7714
Meng Pang,Binghui Wang,Yiu-Ming Cheung,et al. Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction[J]. IEEE Access,2017.
APA Meng Pang,Binghui Wang,Yiu-Ming Cheung,&Chuang Lin.(2017).Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction.IEEE Access.
MLA Meng Pang,et al."Discriminant Manifold Learning via Sparse Coding for Robust Feature Extraction".IEEE Access (2017).
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