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461
Imputation of missing values for cochlear implant candidate audiometric data and potential applications.
Published 2023-01-01“…With 3-8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB- 5.8 dB, non-overlapping 99% confidence intervals). …”
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462
Mixture prior for sparse signals with dependent covariance structure.
Published 2023-01-01“…It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. …”
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463
Mixture prior for sparse signals with dependent covariance structure
Published 2023-01-01“…It is practical for doing this because of the existence of sparsity. Then the sparsity is estimated using an empirical Bayesian method based on the likelihood of the signals with the common dependence removed. …”
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464
Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function
Published 2022-12-01“…In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. …”
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465
An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks
Published 2022-03-01“…It can be seen that the sparsity adaptive pre-selected stagewise orthogonal matching pursuit algorithm has better adaptive characteristics to the sparsity of the signal, which is beneficial for users to receive more accurate original signals.…”
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466
Structured sparse signal recovery algorithms and their applications
Published 2014“…As a natural generation of block sparse signal, hierarchical sparse signal, exhibiting two levels of sparsity, i.e., block sparsity among different blocks and internal sparsity within individual block, is of interest. …”
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Thesis -
467
Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks
Published 2018-10-01“…In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. …”
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468
Tensor network compressed sensing with unsupervised machine learning
Published 2020-08-01“…To characterize the efficiency of TNCS, we propose a quantity named as q sparsity to describe the sparsity of quantum states, which is analogous to the sparsity of the signals required in the standard compressed sensing. …”
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469
Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals Using Structural MRI
Published 2023-09-01“…For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). …”
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470
Delay-Aware Wide-Area Control of Power Systems over Sparse Communications with Analytical Guarantees
Published 2018“…Both sparsity features are introduced in the control design using the well-known Geromel algorithm. …”
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471
Enhancing sparse representation of color images by cross channel transformation
Published 2023-01-01“…Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. …”
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472
Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements
Published 2024-01-01“…This sparsity helps recover a signal even with just a few measurements. …”
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473
WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program
Published 2023“…This thesis presents WACO, a novel method of co-optimizing the format and schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this thesis is the design of a lightweight cost model that accurately predicts the runtime of a sparse tensor program by considering the sparsity pattern, the format, and the schedule. …”
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Thesis -
474
Enhancing sparse representation of color images by cross channel transformation.
Published 2023-01-01“…Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. …”
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Article -
475
Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
Published 2020-04-01“…Second, it employs the L1-norm to describe the sparsity of image gradients, while the L1-norm has a limited capacity of depicting the sparsity of sparse variables. …”
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476
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477
Locally weighted PCA regression to recover missing markers in human motion data.
Published 2022-01-01“…The main merit is to introduce the sparsity of observation datasets through the multivariate tapering approach into traditional least square methods and develop it into a new kind of least square methods with the sparsity constraints. …”
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478
A Novel Approach for Solving Convex Problems with Cardinality Constraints
Published 2017“…In this paper we consider the problem of minimizing a convex differentiable function subject to sparsity constraints. Such constraints are non-convex and the resulting optimization problem is known to be hard to solve. …”
Conference item -
479
A new framework of smoothed location model with multiple correspondence analysis
Published 2016“…We refer this situation as large sparsity problem. When large sparsity of multinomial cells occurs, the smoothed estimators of location model will be greatly biased, hence creating frustrating performance. …”
Conference or Workshop Item -
480