Results 31 to 40 of about 25,940 (257)
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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Sparse PCA: Algorithms, Adversarial Perturbations and Certificates [PDF]
2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), 2020 We study efficient algorithms for Sparse PCA in standard statistical models (spiked covariance in its Wishart form). Our goal is to achieve optimal recovery guarantees while being resilient to small perturbations. Despite a long history of prior works, including explicit studies of perturbation resilience, the best known algorithmic guarantees for ...
Tommaso d'Orsi +3 more
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IEEE Access, 2017 Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis.
Wei Zhou +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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Craniofacial similarity analysis through sparse principal component analysis.
PLoS ONE, 2017 The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery.
Junli Zhao +7 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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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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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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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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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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