Results 41 to 50 of about 513,547 (285)
Image Classification Based on Sparse Representation in the Quaternion Wavelet Domain
IEEE Access, 2022 In this study, we propose a novel sparse representation learning method in the Quaternion Wavelet (QW) domain for multi-class image classification. The proposed method takes advantages from: i) the QW decomposition, which promotes sparsity and provides ...
Long H. Ngo +4 more
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Extreme Learning Machine Based on Stacked Denoising Sparse Auto-Encoder [PDF]
Jisuanji gongcheng, 2020 Extreme Learning Machine(ELM)randomly selects input weights and hidden-layer bias of network,which increases the complexity and reduces the robustness of network.To address the problem,this paper proposes an ELM algorithm based on stacked Denoising ...
ZHANG Guoling, WANG Xiaodan, LI Rui, LAI Jie, XIANG Qian
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IET Radar, Sonar & Navigation, 2017 Microwave radar 3D imaging with high resolution generally requires a great number of samples. The authors aim at accurate reconstruction of microwave radar images while significantly reducing the required number of samples.
He Tian, Daojing Li
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Model study of the leather degradation by oxidation and hydrolysis
Heritage Science, 2019 Many objects of culture heritage, comprised of leather, need to receive the right treatment to be restored and to elongate their lifespan. Determination of the degradation degree and even better the type of the degradation is a crucial knowledge for the ...
Gabriela Vyskočilová +4 more
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Characteristic gene selection via weighting principal components by singular values. [PDF]
PLoS ONE, 2012 Conventional gene selection methods based on principal component analysis (PCA) use only the first principal component (PC) of PCA or sparse PCA to select characteristic genes.
Jin-Xing Liu +4 more
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Optimal Sparse Linear Auto-Encoders and Sparse PCA
CoRR, 2015 Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features. Enforcing sparsity on the principal components can promote better generalization, while improving the interpretability of the features. We study the problem of constructing optimal sparse linear auto-encoders.
Malik Magdon-Ismail, Christos Boutsidis
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Sparse PCA via Bipartite Matchings
CoRR, 2015 We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but ...
Megasthenis Asteris +3 more
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A Robust and Sparse Process Fault Detection Method Based on RSPCA
IEEE Access, 2019 As a method widely used in fault detection, principal component analysis (PCA) still has challenges in applicability due to its sensitivity to outliers and its difficulty in principal components (PCs) interpretation.
Peng Peng +4 more
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On the Worst-Case Approximability of Sparse PCA
CoRR, 2015 20 ...
Siu On Chan +2 more
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A Communication-Efficient and Privacy-Aware Distributed Algorithm for Sparse PCA [PDF]
, 2022 Sparse principal component analysis (PCA) improves interpretability of the classic PCA by introducing sparsity into the dimension-reduction process. Optimization models for sparse PCA, however, are generally non-convex, non-smooth and more difficult to ...
Zhang, Yin, Wang, Lei, Liu, Xin
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