Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing by Risheng Huang, Xiaorun Li, Liaoying Zhao

“…In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. …”
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Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition by Gulizada Haisa, Gulila Altenbek

“…A common strategy to handle data sparsity is to apply subword segmentation. Thus, we combined the semantics of words and stems by stemming from the Kazakh morphological analysis system. …”
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A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation by Yunfa Wu, Bin Zhang, Anbo Meng, Yong-Hua Liu, Chun-Yi Su

“…Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. …”
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Lightweight privacy-preserving truth discovery for vehicular air quality monitoring by Rui Liu, Jianping Pan

“…However, in urban cities, there is a significant difference in traffic volumes of streets or blocks, which leads to a data sparsity problem for truth discovery. Protecting the privacy of participant vehicles is also a crucial task. …”
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Multi-objective cluster based bidding algorithm for E-commerce search engine marketing system by Cheng Jie, Zigeng Wang, Da Xu, Wei Shen

“…It leverages their geometric relation to building collaborative bidding predictions via clustering to address performance features' sparsity issues. We provide theoretical and numerical analyzes to discuss how we find the proposed system as a production-efficient solution.…”
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Hybrid Recommender System Based on Attention Mechanisms and Gating Network by GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao

“…Combining user reviews with user ratings to improve the performance of recommender system is the current mainstream research direction of recommender system.However,when user review data is sparse,the performance of most existing recommender systems will degrade to a certain extent.To solve this problem,this paper proposes a hybrid recommendation system (AMGNRS),which combines attention mechanism and gating networking based recommendation system.It use auxiliary comments generated by like-minded users to alleviate the sparsity of user comments.Firstly,a variety of mixed attention mechanism are combined to impove the feature extracting efficiency of user comments and grading.Then features are extracted by adaptive fusion of gated network,and features most relevant to user preference are selected.Finally,the higher order linear interaction of the neural factorization machine is used to derive the score prediction.By comparing the model with the current model with excellent performance on three real data sets,the results show that the problem of data sparsity is significantly alleviated and the effectiveness of the model is verified.…”
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DCS-based MBSBL joint reconstruction of multi-sensors data for energy-efficient telemonitoring of human activity by Jianning Wu, Jiajing Wang, Yun Ling

“…Its basic idea is that based on the joint sparsity model, the distributed compressed sensing technique is first applied to simultaneously compress the multi-sensors data for gaining the high-correlation information regarding activity as well as the energy efficiency of sensors, and then, the multiple block sparse Bayesian learning technique is employed to jointly recover nonsparse multi-sensors data with high fidelity by exploiting the joint block sparsity. …”
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Out-of-time-order correlators and Lyapunov exponents in sparse SYK by Elena Cáceres, Tyler Guglielmo, Brian Kent, Anderson Misobuchi

“…We also study OTOCs numerically as a function of the sparsity parameter and determine the Lyapunov exponent. …”
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Efficient sparse coding in early sensory processing: lessons from signal recovery. by András Lörincz, Zsolt Palotai, Gábor Szirtes

“…We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. …”
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Finding sparse, equivalent SDPs using minimal coordinate projections by Permenter, Frank Noble, Parrilo, Pablo A.

“…We present a new method for simplifying SDPs that blends aspects of symmetry reduction with sparsity exploitation. By identifying a subspace of sparse matrices that provably intersects (but doesn't necessarily contain) the set of optimal solutions, we both block-diagonalize semidefinite constraints and enhance problem sparsity for many SDPs arising in sums-of-squares optimization. …”
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Learning customized and optimized lists of rules with mathematical programming by Rudin, Cynthia, Ertekin, Şeyda

“…Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear user-defined tradeoff between training accuracy and sparsity. …”
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Explicit Construction of RIP Matrices Is Ramsey‐Hard by Gamarnik, David

“…While it is known that random matrices satisfy the RIP with high probability even for n = logO(1)p, the explicit deteministic construction of such matrices defied the repeated efforts, and most of the known approaches hit the so-called (Formula presented.) sparsity bottleneck. The notable exception is the work by Bourgain et al. constructing an n × p RIP matrix with sparsity s = Θ(n1/2 + ϵ), but in the regime n = Ω(p1 − δ). …”
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Constrained magnetic resonance image reconstruction from incomplete frequency measurements by Deng, Jun

“…In this thesis, we develop constrained reconstructions methods to reconstruct high quality MR images from down-sampled k-space data by exploiting sparsity of MR images under the theory of Compressed Sensing. …”
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Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI by Shahdloo, M, Ilicak, E, Tofighi, M, Saritas, EU, Cetin, AE, Cukur, T

“…The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. …”

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks by Engelcke, M, Rao, D, Wang, D, Tong, C, Posner, H

“…In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L 1 penalty on the filter activations to further encourage sparsity in the intermediate representations. …”

Hadamard wirtinger flow for sparse phase retrieval by Wu, F, Rebeschini, P

“…Provided knowledge of the signal sparsity k, we prove that a single step of HWF is able to recover the support from k(x ∗ max) −2 (modulo logarithmic term) samples, where x ∗ max is the largest component of the signal in magnitude. …”

Responsive and intelligent service recommendation method based on deep learning in cloud service by Lei Yu, Yucong Duan

“…The first challenge is the sparsity of data; the sparsity makes it difficult for CF to accurately determine whether users are similar, and the gap between the hidden matrices obtained by MF decomposition is large; the second challenge is the cold start of recommendation when new users (or services) participate in the recommendation; its historical record is vacant, making accurately predicting the QoS value be more difficult. …”
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Blended Glial Cell’s Spiking Neural Network by Liying Tao, Pan Li, Meihua Meng, Zonglin Yang, Xiaozhuang Liu, Jinhua Hu, Ji Dong, Shushan Qiao, Tianchun Ye, Delong Shang

“…In addition, BGSNN exhibits good parallelism and sparsity, decreasing computation by at least 92.9% compared to serial solvers and reducing sparsity by 88% compared to the equal fully dense DNN. …”
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Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry by Eike Bolmer, Adili Abulaitijiang, Jürgen Kusche, Ribana Roscher, Ribana Roscher

“…This network is capable of producing a two-dimensional segmentation map from data with varying sparsity. We have developed an architecture called Teddy, which uses a Transformer module to encode and process spatiotemporal information, and a sparsity invariant CNN to infer a two-dimensional segmentation map of classified eddies from the ground tracks of varying sparsity on the considered region. …”
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Abnormal event detection in crowded scenes using sparse representation by Cong, Yang, Yuan, Junsong, Liu, Ji

“…To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. …”
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