Mixture of Species Sampling Models by Federico Bassetti, Lucia Ladelli

“…These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). …”
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METHOD FOR PLANE NEAR-FIELD ACOUSTIC HOLOGRAPHY BASED ON COMPRESSIVE SAMPLING AND ITS APPLICATION by DU Bao, LUO Jian, HU Fei, LIU XiaoQin, WU Xing

“…Plane near-field acoustic holography based on Compressive Sampling,translates traditional solved particle velocity into solving sparse coefficient,by effective using of the particle velocity sparsity,avoiding the reconstruction process of ill-posed problem. …”
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Dynamic mode decomposition of numerical data in natural circulation by José Luiz Horacio Faccini

“…In this paper it is applied the traditional DMD and its variation, the sparsity-promoting dynamic mode decomposition (SPDMD), for analysis of temperature and velocity fields data, generated by computational simulation of an experimental setup in reduced scale, similar to a heat removal system by natural circulation of a pool-type research reactor. …”
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Preprocessed Spectral Clustering with Higher Connectivity for Robustness in Real-World Applications by Fatemeh Sadjadi, Vicenç Torra, Mina Jamshidi

“…The proposed method leverages both sparsity and connectivity properties within each cluster to find a consensus similarity matrix. …”
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Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL by Shuai Hou, Yafeng Wang, Chao Li

“…To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. …”
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Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example by Marwan Al-Momani, Mohammad Arashi

“…Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. …”
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Detecting trends and shocks in terrorist activities. by Rafael Prieto-Curiel, Olivier Walther, Ewan Davies

“…Although there are some techniques for dealing with sparse and concentrated discrete data, standard time-series analyses appear ill-suited to understanding the temporal patterns of terrorist attacks due to the sparsity of the events. This article addresses these issues by proposing a novel technique for analysing low-frequency temporal events, such as terrorism, based on their cumulative curve and corresponding gradients. …”
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Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization by Gihwan Lee, Yoonsik Choe

“…To determine a trade-off parameter between the reconstruction error and sparsity, which hinders efficient implementation, the proposed method adapts Bayesian optimization. …”
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Weighted Structured Sparse Reconstruction-Based Lamb Wave Imaging Exploiting Multipath Edge Reflections in an Isotropic Plate by Caibin Xu, Zhibo Yang, Mingxi Deng

“…A dictionary is constructed by an analytical Lamb wave scattering model and an edge reflection prediction technique, which is used to decompose the experimental scattering signals under the constraint of weighted structured sparsity. The weights are generated from the correlation coefficients between the scattering signals and the predicted ones. …”
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Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value by Krzysztof Gromada

“…The scarcity of open SAR (Synthetic Aperture Radars) imagery databases (especially the labeled ones) and sparsity of pre-trained neural networks lead to the need for heavy data generation, augmentation, or transfer learning usage. …”
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Sparse sampling for fast quasiparticle-interference mapping by Jens Oppliger, Fabian Donat Natterer

“…The requirement of CS is naturally fulfilled for QPI, since CS relies on sparsity in a vector domain, here given by few nonzero coefficients in Fourier space. …”
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Joint routing and scheduling for data collection with compressive sensing to achieve order-optimal latency by Xiaohan Yu, Seung Jun Baek

“…In addition, the proposed scheme is shown to be energy-efficient, in that it can achieve order-optimal energy consumption given that the sensor data sparsity is of constant order. Simulation results show the effectiveness of the proposed scheme in terms of latency and energy consumption.…”
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The Convergence Rates of Large Volatility Matrix Estimator Based on Noise, Jumps, and Asynchronization by Erlin Guo, Cuixia Li, Fengqin Tang

“…Finally, we employ the threshold parameters to remove the effect of jumps and sparsity in two steps. Both the minimax bound and the convergence rate are discussed in the paper. …”
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Knowledge reasoning with multiple relational paths by Hang Su, Huangcan Li, Dun Li

“…Finally, comparative experiments are carried out on the public dataset, and the results show that our model is superior to other models in relation prediction tasks and link prediction tasks, improves the computational efficiency and data sparsity, and provides a new idea for knowledge reasoning methods.…”
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Double Regularization Matrix Factorization Recommendation Algorithm by Ruizhong Du, Jiaojiao Lu, Hongyun Cai

“…Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.…”
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A RPCA-Based ISAR Imaging Method for Micromotion Targets by Liangyou Lu, Peng Chen, Lenan Wu

“…To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. …”
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An Overview on Sparse Recovery-based STAP by Ma Ze-qiang, Wang Xi-qin, Liu Yi-min, Meng Hua-dong

“…A major part of this paper presents the state-of-art research results in spatio-temporal spectrum-sparsity-based STAP, including the basic frame, off-grid problem, multiple measurement vector problem, and direct domain problem. …”
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Non-stationary Sparse System Identification over Adaptive Sensor Networks with Diffusion and Incremental Strategies by Amir Bazdar, Amin Aliabadi, Ehsan Mostafapour, Changiz Gobadi

“…The performance analyses are carried out with the steady-state mean square deviation (MSD) criterion of adaptive algorithms. Some sparsity aware algorithms are considered in this paper which tested in non-stationary systems for the first time. …”
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Implementation of the Spark technique in a matrix distributed computing algorithm by Wang Ying, Cengiz Korhan

“…When the density of the fixed sparse matrix is 0.01, the distributed density-sparse matrix multiplication outperforms the same sparsity but uses the density matrix storage, and the acceleration ratio increases from 1.88× to 5.71× with the increase in dimension. …”
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Nonparametric Additive Regression for High-Dimensional Group Testing Data by Xinlei Zuo, Juan Ding, Junjian Zhang, Wenjun Xiong

“…Nonlinear components are approximated using B-splines and model estimation under the sparsity assumption is derived employing group lasso. …”
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