Sparse ECG Denoising with Generalized Minimax Concave Penalty by Zhongyi Jin, Anming Dong, Minglei Shu, Yinglong Wang

“…A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional <inline-formula> <math display="inline"> <semantics> <msub> <mo>ℓ</mo> <mn>1</mn> </msub> </semantics> </math> </inline-formula>-norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. …”
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Mirror descent for high-dimensional statistical models by Wu, F

“…In contrast to existing algorithms for sparse phase retrieval, HWF does not require any explicit regularization in the form of added regularization terms or thresholding steps to enforce sparsity of its estimates and is empirically seen to adapt to the sparsity level of the underlying signal. …”

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses by Chaoran Yang, Qing Ling, Xueli Sheng, Mengfei Mu, Andreas Jakobsson

“…This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. …”
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Off-grid DOA estimation using improved root sparse Bayesian learning for non-uniform linear arrays by Jiajun Shen, Fulvio Gini, Maria Sabrina Greco, Tian Zhou

“…Then, we integrate a constant false alarm rate rule in the SBL framework to enforce sparsity and improve computational efficiency. Finally, we generalize the IRSBL method to the case of non-uniform linear arrays. …”
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Sparse Index Tracking Portfolio with Sector Neutrality by Yuezhang Che, Shuyan Chen, Xin Liu

“…As a popular passive investment strategy, a sparse index tracking strategy has advantages over a full index replication strategy because of higher liquidity and lower transaction costs. Sparsity and nonnegativity constraints are usually assumed in the construction of portfolios in sparse index tracking. …”
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FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework by Jianlong Xu, Kun Wang, Che Lin, Lianghong Xiao, Xingshan Huang, Yufeng Zhang

“…However, due to the variety in water quality data, inconsistent frequency of data acquisition, inconsistency in data organization, and volatility and sparsity of data, predicting water quality accurately and efficiently has become a key problem. …”
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A Code Reviewer Recommendation Approach Based on Attentive Neighbor Embedding Propagation by Jiahui Liu, Ansheng Deng, Qiuju Xie, Guanli Yue

“…However, such approaches usually suffer from data-sparsity and noise problems, which may reduce the recommendation accuracy. …”
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Measurement Matrix Construction for Large-area Single Photon Compressive Imaging by Hui Wang, Qiurong Yan, Bing Li, Chenglong Yuan, Yuhao Wang

“…The experimental results show that, the increase of sparsity ratio and compressive sampling ratio can improve the reconstruction quality. …”
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GATCF: Graph Attention Collaborative Filtering for Reliable Blockchain Services Selection in BaaS by Yuxiang Zeng, Jianlong Xu, Zhuohua Zhang, Caiyi Chen, Qianyu Ling, Jialin Wang

“…Choosing the best-trusted blockchain peers is a challenging task due to the sparsity of data caused by the multitude of available options. …”
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A word embedding topic model for topic detection and summary in social networks by Lei Shi, Gang Cheng, Shang-ru Xie, Gang Xie

“…We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts. To detect and summarize the topic, we propose a topic detection method by leveraging similarity computing for social networks. …”
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Data Gathering in Wireless Sensor Networks Based on Reshuffling Cluster Compressed Sensing by Lu Zhu, Baishan Ci, Yuanyuan Liu, Zhizhang (David) Chen

“…The existing compressed sensing (CS) based data gathering (CSDG) methods in wireless sensor networks (WSNs) usually assume that the sensed data are sparse or compressible. However, the sparsity of raw sensed data in some case is not straightforward. …”
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CBEA: Competitive balances for taxonomic enrichment analysis. by Quang P Nguyen, Anne G Hoen, H Robert Frost

“…An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. …”
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DE-JSMA: a sparse adversarial attack algorithm for SAR-ATR models by JIN Xiaying, LI Yang, PAN Quan

“…In order to verify the vulnerability, this paper proposes DE-JSMA, a novel sparse adversarial attack algorithm based on a salient map's adversarial attack algorithm and differential evolution algorithm, with the synthetic aperture radar (SAR) image feature sparsity considered. After accurately screening out the salient features that have a great impact on the model inference results, the DE-JSMA algorithm optimizes the appropriate feature values for the salient features. …”
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Zero-Inflated Patent Data Analysis Using Compound Poisson Models by Sangsung Park, Sunghae Jun

“…In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. …”
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Service Discovery Method Based on Knowledge Graph and Word2vec by Junkai Zhou, Bo Jiang, Jie Yang, Junchen Yang, Hang Li, Ning Wang, Jiale Wang

“…For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. …”
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Infrared Image Super Resolution by Combining Compressive Sensing and Deep Learning by Xudong Zhang, Chunlai Li, Qingpeng Meng, Shijie Liu, Yue Zhang, Jianyu Wang

“…However, because of diverse level of sparsity for different images, the output contains noise and loss of high frequency information. …”
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Blind Hyperspectral Unmixing with Enhanced 2DTV Regularization Term by Peng Wang, Xun Shen, Yingying Kong, Xiwang Zhang, Liguo Wang

“…The E-2DTV regularization term is based on the gradient mapping of all bands of HSI, and the sparsity is calculated on the basis of the subspace, rather than applying sparsity to the gradient map itself, which can utilize the correlations and differences between all bands naturally. …”
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Context-Based User Typicality Collaborative Filtering Recommendation by Jinzhen Zhang, Qinghua Zhang, Zhihua Ai, Xintai Li

“…User typicality indicates the preference of user for different item types, which could reflect the preference of user at a higher abstraction level than the items rated by user, and can alleviate data sparsity. But it does not consider the impact of contextual information on user typicality. …”
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The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization by Yunfei Zhang, Hongzhen Xu, Xiaojun Yu

“…By learning the distribution characteristics of the data, missing values in the rating data can be effectively reconstructed, thereby reducing the sparsity of the rating matrix. Subsequently, the reconstructed data is processed using CPMF, which optimizes the feature extraction performance by imposing constraints on user features. …”
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A Robust Reweighted L1-Minimization Imaging Algorithm for Passive Millimeter Wave SAIR in Near Field by Yilong Zhang, Yuehua Li, Shujin Zhu, Yuanjiang Li

“…RRIA employs iterative reweighted L1-minimization to enhance the sparsity to reconstruct SAIR images by computing a new weight factor in each iteration according to the previous SAIR images. …”
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