Nearly minimax-optimal rates for noisy sparse phase retrieval via early-stopped mirror descent by Wu, F, Rebeschini, P

“…Our theory leads to a simple algorithm that does not rely on explicit regularization or thresholding steps to promote sparsity. More generally, our results establish a connection between mirror descent and sparsity in the non-convex problem of noisy sparse phase retrieval, adding to the literature on early stopping that has mostly focused on non-sparse, Euclidean, and convex settings via gradient descent. …”

Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach by Zhiwen Wang, Shouwang Sun, Yiwei Li, Zixiang Yue, Youliang Ding

“…Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. …”
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An Appearance-Semantic Descriptor with Coarse-to-Fine Matching for Robust VPR by Jie Chen, Wenbo Li, Pengshuai Hou, Zipeng Yang, Haoyu Zhao

“…However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. …”
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Crossbar-aligned & integer-only neural network compression for efficient in-memory acceleration by Huai, Shuo, Liu, Di, Luo, Xiangzhong, Chen, Hui, Liu, Weichen, Subramaniam, Ravi

“…Finally, we design a learning method to complete above two schemes and cultivate an optimal compact DNN with high accuracy and large sparsity during training. Experiments demonstrate that our framework, compared to state-of-the-art methods, achieves larger sparsity and lower power consumption with higher accuracy. …”
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Temporal integration based factorization to improve prediction accuracy of collaborative filtering by Al-Qasem, Al-Hadi Ismail Ahmed

“…The rating matrix typically contains a high percentage of unknown rating scores which is called the data sparsity problem. The data sparsity problem has been solved by several approaches such as Bayesian probabilistic, machine learning, genetic algorithm, particle swarm optimization and matrix factorization. …”
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A Multiple Comprehensive Analysis of scATAC-seq Based on Auto-Encoder and Matrix Decomposition by Yuyao Huang, Yizhou Li, Yuan Liu, Runyu Jing, Menglong Li

“…As a result, the method designed for handling the sparsity outperforms other tools in the generated dataset.…”
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A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem by Weiming Huang, Baisong Liu, Zhaoliang Wang

“…However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. …”
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A Knowledge Graph Recommendation Approach Incorporating Contrastive and Relationship Learning by Xintao Shen, Yulai Zhang

“…However, most current recommendation approaches reliant on knowledge graphs are susceptible to the disturbances of noise and data sparsity present in low-quality knowledge graphs, thereby undermining the efficacy of these recommendations. …”
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Feature Extracted Deep Neural Collaborative Filtering for E-Book Service Recommendations by Ji-Yoon Kim, Chae-Kwan Lim

“…However, due to data sparsity, the recommendation systems have low accuracy. …”
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Multiple sparse detection-based evolutionary algorithm for large-scale sparse multiobjective optimization problems by Jin Ren, Feiyue Qiu, Huizhen Hu

“…The algorithm applies an adaptive sparse genetic operator that can generate sparse solutions by detecting the sparsity of individuals. To improve the deficiency of sparse detection caused by local detection, an enhanced sparse detection (ESD) strategy is proposed in this paper. …”
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A Review of Radar Signal Processing Based on Sparse Recovery by Yinghui QUAN, Yaojun WU, Lining DUAN, Gang XU, Min XUE, Zhixing LIU, Mengdao XING

“…This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. …”
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A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet by Harshini Gangapuram, Vidya Manian

“…However, compressive sensing is limited, despite its flexibility and data efficiency, because of its sparsity and high computational cost in reconstructing signals. …”
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Block-Based Compression and Corresponding Hardware Circuits for Sparse Activations by Yui-Kai Weng, Shih-Hsu Huang, Hsu-Yu Kao

“…In a CNN (convolutional neural network) accelerator, to reduce memory traffic and power consumption, there is a need to exploit the sparsity of activation values. Therefore, some research efforts have been paid to skip ineffectual computations (i.e., multiplications by zero). …”
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A Block Sparse-Based Dynamic Compressed Sensing Channel Estimator for Underwater Acoustic Communication by Lingji Xu, Lixing Chen, Yaan Li, Weihua Jiang

“…Due to the complex ocean propagation environments, the underwater acoustic (UWA) multipath channel often exhibits block sparse time-varying features, and while dynamic compressed sensing (DCS) can mitigate the time-varying effects of the UWA channel, DCS-based algorithms have limited performance for the UWA channel with block sparsity. In this study, by formulating the UWA channel with blocks concatenation, a block sparse-based DCS approach (BS-CS) is proposed to explore the block and time-varying sparsity of UWA channel simultaneously. …”
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Efficient Distributed Multi-Task Schemes for mmWave MIMO Channel Estimation by Maria Trigka, Christos Mavrokefalidis, Kostas Berberidis

“…We exploit the spatially joint sparsity structure of the involved channels to formulate the channel estimation problem in the angular domain. …”
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On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets by Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos

“…The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. …”
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Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation by Guichi Liu, Lei Gao, Lin Qi

“…However, SRC only focuses on sparsity but ignores the data correlation information. …”
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Multi-features taxi destination prediction with frequency domain processing. by Lei Zhang, Guoxing Zhang, Zhizheng Liang, Ekene Frank Ozioko

“…However, the trajectory image may have noise and sparsity according to trajectory data characteristics. …”
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Depth Image Coding Using Entropy-Based Adaptive Measurement Allocation by Huihui Bai, Mengmeng Zhang, Meiqin Liu, Anhong Wang, Yao Zhao

“…At the encoder, in view of the characteristics of depth images, the entropy of pixels in each block is employed to represent the sparsity of depth signals. Then according to the different sparsity in the pixel domain, the measurements can be adaptively allocated to each block for higher compression efficiency. …”
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A Useful Criterion on Studying Consistent Estimation in Community Detection by Huan Qing

“…We summarize the idea of using a separation condition for a standard network and sharp threshold of the Erdös–Rényi random graph to study consistent estimation, and compare theoretical error rates and requirements on the network sparsity of spectral methods under models that can degenerate to a stochastic block model as a four-step criterion SCSTC. …”
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