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Sparsity-Based Data Reconstruction Models for Biomedical Imaging
2016We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in the field.
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Investigations on the Sensitivity of Sparsity Measures to the Sparsity of Impulsive Signals
Mechanical Systems and Signal Processing, 2022Zhike Peng, Tongtong Yan, Changqing Shen
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Resolving Data Sparsity and Cold Start in Recommender Systems
2012Recommender systems (RSs) are heavily used in e-commerce to provide users with high quality, personalized recommendations from a large number of choices. Collaborative filtering (CF) is a widely used technique to generate recommendations [1]. The main research problems we desire to address are the two severe issues that original CF inherently suffers ...
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Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion
IEEE Transactions on Neural Networks and Learning Systems, 2022Shaoguang Huang +2 more
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Research on Sparsity Indexes for Fault Diagnosis of Rotating Machinery
Measurement: Journal of the International Measurement Confederation, 2020Ming Zhao, Jiadong Hua, Yonghao Miao
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Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models
IEEE Transactions on Image Processing, 2021Ce Zhu, Zhiyuan Zha, Jiantao Zhou
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Enhanced Sparsity Prior Model for Low-Rank Tensor Completion
IEEE Transactions on Neural Networks and Learning Systems, 2020Seong G Kong +2 more
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