Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions by Balraj Kumar, Neeraj Sharma, Bhisham Sharma, Norbert Herencsar, Gautam Srivastava

“…RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. …”
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A PPN-Based Improved QAM-FBMC System With Jointly Optimized Mismatched Prototype Filters by Taejun Jang, Joon Ho Cho

“…The sparsity constraint on the RX prototype filter is now removed, and a new polyphase network (PPN)-based structure is introduced to maintain the complexity at the RX to almost the same level. …”
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A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis by Yijie Niu, Wu Deng, Xuesong Zhang, Yuchun Wang, Guoqing Wang, Yanjuan Wang, Pengpeng Zhi

“…Sparsity-based fault diagnosis methods have achieved great success. …”
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MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning by Ziwei Chen, Liangzhe Zhang, Jingyi Li, Mingyang Fu

“…Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. …”
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Forward-Looking Super-Resolution Imaging for Sea-Surface Target with Multi-Prior Bayesian Method by Weixin Li, Ming Li, Lei Zuo, Hao Sun, Hongmeng Chen, Yachao Li

“…Secondly, we fuse the total variation (TV) prior and Laplace prior, and propose a multi-prior to model the contour information and sparsity of the target. Third, we introduce the latent variable to simplify the logarithm likelihood function. …”
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Supersparse linear integer models for optimized medical scoring systems by Ustun, Berk, Rudin, Cynthia

“…SLIM scoring systems are built by using an integer programming problem that directly encodes measures of accuracy (the 0–1 loss) and sparsity (the ℓ[subscript 0]-seminorm) while restricting coefficients to coprime integers. …”
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Nearly tight oblivious subspace embeddings by trace inequalities by Cohen, Michael Benjamin.

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A learning hierarchy for classification and regression by Ajjanagadde, Ganesh

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Compressive algorithms for search and storage in biological data by Yu, Yun William

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Post-Selection Inference for Generalized Linear Models With Many Controls by Belloni, Alexandre, Wei, Ying, Chernozhukov, Victor V

“…These methods allow to estimate α[subscript 0] at the root-n rate when the total number p of other regressors, called controls, potentially exceeds the sample size n using sparsity assumptions. The sparsity assumption means that there is a subset of s < n controls, which suffices to accurately approximate the nuisance part of the regression function. …”
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Scaling law for recovering the sparsest element in a subspace by Demanet, Laurent, Hand, Paul

“…If sparsity is interpreted in an ℓ1/ℓ∞ sense, then the scaling law cannot be better than s≲n/√k. …”
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Photon-efficient super-resolution laser radar by Shin, Dongeek, Shapiro, Jeffrey H, Goyal, Vivek K

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EGC: Sparse covariance estimation in logit mixture models by Aboutaleb, Youssef M, Danaf, Mazen, Xie, Yifei, Ben-Akiva, Moshe E

“…The optimal sparsity level of the covariance matrix is determined using out-of-sample validation. …”
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Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices by Chen, Yu-Hsin, Yang, Tien-Ju, Emer, Joel S, Sze, Vivienne

“…These endeavors aim to reduce the DNN model size and improve the hardware processing efficiency and have resulted in DNNs that are much more compact in their structures and/or have high data sparsity. These compact or sparse models are different from the traditional large ones in that there is much more variation in their layer shapes and sizes and often require specialized hardware to exploit sparsity for performance improvement. …”
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Efficient reinforcement learning via singular value decomposition, end-to-end model-based methods and reward shaping by Gehring, Clement

“…Specifically, this work examines the low-rank structure found in various aspects of decision making problems and the sparsity of effects of classical deterministic planning, as well as the properties that end-to-end model-based methods depend on to perform well. …”
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Diffuse imaging: Replacing lenses and mirrors with omnitemporal cameras by Kirmani, Ahmed, Jeelani, Haris, Montazerhodjat, Vahid, Goyal, Vivek K.

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Sparse equalizer filter design for multi-path channels by Feng, Xue, Ph. D. Massachusetts Institute of Technology

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Weighted block sparse recovery algorithm for high resolution doa estimation with unknown mutual coupling by Meng, Dandan, Wang, Xianpeng, Huang, Mengxing, Shen, Chong, Bi, Guoan

“…Due to the use of the whole received data of array and the enhanced sparsity of solution, the proposed method effectively avoids the loss of the array aperture to achieve a better estimation performance in the environment of unknown mutual coupling in terms of both spatial resolution and accuracy. …”
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Tensor decomposition for spatial-temporal traffic flow prediction with sparse data by Yang, Funing, Liu, Guoliang, Huang, Liping, Chin, Cheng Siong

“…The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. …”
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Image recovery via transform learning and low-rank modeling: the power of complementary regularizers by Wen, Bihan, Li, Yanjun, Bresler, Yoram

“…Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. …”
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