Hyperspectral Imagery Super-Resolution by Compressive Sensing Inspired Dictionary Learning and Spatial-Spectral Regularization by Wei Huang, Liang Xiao, Hongyi Liu, Zhihui Wei

“…Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. …”
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EP‐LSTM: Novel prediction algorithm for moving object destination by Bingrong Li, Dechang Pi, Mengru Hou

“…At present, there are problems such as data sparsity and long‐term dependence based on historical trajectory prediction methods, which affect the accuracy of prediction. …”
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Doppler and Channel Estimation Using Superimposed Linear Frequency Modulation Preamble Signal for Underwater Acoustic Communication by Chenglei Lv, Qiushi Sun, Huifang Chen, Lei Xie

“…Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.…”
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SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition by Zhaoqilin Yang, Gaoyun An, Ruichen Zhang, Zhenxing Zheng, Qiuqi Ruan

“…However, as far as is known, no one has studied the influence of the sparsity of network output on the output of deep learning‐based action recognition models. …”
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Block Sparse Compressed Sensing of Electroencephalogram (EEG) Signals by Exploiting Linear and Non-Linear Dependencies by Hesham Mahrous, Rabab Ward

“…This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency) within the measurement vector of a single channel. …”
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Sampling great circles at their rate of innovation by Deslauriers-Gauthier, Samuel, Marziliano, Pina

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Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison by Yong Zheng

“…However, it suffers from the sparsity problem since users may not rate items in various context situations. …”
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Corrected rank residual constraint model for image denoising by Di Wu, Tao Zhang

“…The studies show that the adaptive correction term can not only improve the sparsity of the rank residual but also overcome the over‐shrinkage of large singular values. …”
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Error Estimations for Total Variation Type Regularization by Kuan Li, Chun Huang, Ziyang Yuan

“…Furthermore, an improved convergence rate is given based on the sparsity assumption. The problem under the condition of non-sparsity, which is common in practice, is also discussed; the results of the corresponding convergence rate are also presented under certain mild conditions.…”
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Decomposition and completion of sum-of-squares matrices by Zheng, Y, Fantuzzi, G, Papachristodoulou, A

“…We show that a subset of sparse SOS matrices with chordal sparsity patterns can be equivalently decomposed into a sum of multiple SOS matrices that are nonzero only on a principal submatrix. …”

A New Framework of Smoothed Location Model with Multiple Correspondence Analysis by Hamid, Hashibah

“…We refer this sitnation as large sparsity pmblem. When large sparsity of midhomial d s occurs, the smoothed estimators of location model will be greatly biased, hence creating ffmtmting performance. …”
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A Novel Beam Alignment Scheme for Mobile Millimeter-Wave Communications Based on Compressed Sensing Aided-Kalman Filter by Kun-Hsien Lin, Kuang-Hao Liu

“…Since the CS performance is heavily dependent on the sparsity level, the proposed method increases the signal sparsity level by applying adaptive CS on the observation residual computed from the previous estimate of the support to predict the angles of the dominant paths, while the corresponding path gains are tracked by the reduced-order KF. …”
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Phase Transition of Total Variation Based on Approximate Message Passing Algorithm by Xiang Cheng, Hong Lei

“…In compressed sensing (CS), one seeks to down-sample some high-dimensional signals and recover them accurately by exploiting the sparsity of the signals. However, the traditional sparsity assumption cannot be directly satisfied in most practical applications. …”
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Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced Multi-Target Tracking by Seung Hyo Park, Sa Yong Chong, Hyung June Kim, Taek Lyul Song

“…The clutter measurement density estimation performance and target tracking performance are also analyzed for various sparsity orders.…”
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Shrink and Eliminate: A Study of Post-Training Quantization and Repeated Operations Elimination in RNN Models by Nesma M. Rezk, Tomas Nordström, Zain Ul-Abdin

“…In addition, we show that the quantization of activation vectors in RNNs to integer precision leads to considerable sparsity if the delta networks method is applied. Then, we propose a method for increasing the sparsity in the activation vectors while minimizing the error and maximizing the percentage of eliminated computations. …”
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A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning by En Zhang, Wenming Ma, Jinkai Zhang, Xuchen Xia

“…Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. …”
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Broadband Cooperative Spectrum Sensing Based on Distributed Modulated Wideband Converter by Ziyong Xu, Zhi Li, Jian Li

“…Besides, it is very hard to adjust the number of sampling channels when the sparsity changes, because its undersampling board is designed by a given sparsity. …”
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Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network by LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi

“…With the deep integration of computer technology into social life,more and more short text messages are spreaded all over the web platform.Aiming at the problem of data sparsity of short texts,a robust heterogeneous information network framework(HTE) for modeling short texts,which can integrate any type of additional information and capture the relationship between them to solve the data sparsity problem,is constructed.Based on this framework,six short text expansion methods are designed using different external knowledge,and the short text features are enriched by introducing entity information such as entities,entity categories,inter-entity relationships and textual information such as text topics from Wikipedia and Freebase knowledge bases.Finally,the similarity measurement result is used to verify the experimental effect.By comparing the six text expansion me-thods with the traditional three similarity measures on two short text datasets and the current mainstream short text matching algorithms,the results of the proposed six text expansion methods are improved.Compared with BERT,the similarity measurement results of the best method improves by 5.97%.The proposed framework is robust and can include any type of external know-ledge,and the proposed method can overcome the data sparsity problem of short texts and can perform similarity metrics on short texts with high accuracy in an unsupervised manner.…”
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Multi‐view registration based on weighted LRS matrix decomposition of motions by Congcong Jin, Jihua Zhu, Yaochen Li, Shanmin Pang, Lei Chen, Jun Wang

“…On the basis of the inverse symmetry property of relative motions, it first proposes a completion method to reduce the sparsity of the reconstructed matrix. The reduced sparsity of the reconstructed matrix can improve the robustness and efficiency of LRS matrix decomposition. …”
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Data-Driven Regularization Parameter Selection in Dynamic MRI by Matti Hanhela, Olli Gröhn, Mikko Kettunen, Kati Niinimäki, Marko Vauhkonen, Ville Kolehmainen

“…We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. …”
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