Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions on the design matrix. The Bayesian compressive sensing approach r…
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48 results for “Bayesian compressive sensing”
New method uses generative priors for compressive sensing with sparse solutions.
problem Fundamental linear inverse problem in compressive sensing.
method Sparse Bayesian learning with conditional Gaussianity.
result Ability to learn from few compressed and noisy samples without optimization.
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the ou…
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…
New Bayesian method for sparse signal recovery using normal product priors.
problem Sparse signal recovery in compressive sensing.
method Developed a two-stage normal product-based hierarchical model using variational Bayesian inference.
result Demonstrated effectiveness through simulations compared to state-of-the-art algorithms.
Bayesian EP solves CS problems more accurately than other methods.
problem Finding sparse solutions to underdetermined linear systems with constraints.
method Bayesian inference with Expectation Propagation (EP) for marginal distribution computation.
result EP outperforms other methods in solving CS problems with correlated sensing matrices.
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study…
A new method for matching binary distributions using compressed sensing.
problem Matching fixed-length binary distributions efficiently.
method Inspired by compressed sensing, the paper introduces sparsity in binary sources via position modulation and a simple exact matcher based on Gaussian signal quantization. The dematcher uses GAMP for low-complexity dematching.
result The proposed method achieves asymptotically optimal performance, with vanishing reconstruction error in a proper limit.
This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowl…
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with…
In this work, we consider compressed sensing reconstruction from measurements of -sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how…
KMBBO uses K-means clustering to optimize complex problems efficiently.
problem Optimizing complex problems with high-dimensional data.
method Uses unsupervised learning to estimate peaks of the acquisition function, combined with compressed sensing for dimensionality reduction.
result KMBBO outperforms state-of-the-art batch allocation algorithms in various test problems.
Unrolled networks learn optimal Bayesian inference for unknown priors.
problem Optimizing Bayesian inference when the prior is unknown.
method Unrolling neural networks to simulate iterations of inference algorithms.
result Unrolled networks approximate convergence to optimal denoisers for product priors.
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic…
In this paper, we look to address the problem of estimating the dynamic direction of arrival (DOA) of a narrowband signal impinging on a sensor array from the far field. The initial estimate is made using a Bayesian compressive sensing (BCS) framework and then tracked using a Bayesian compressed sensing Kalman filter (…
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signalsstat.AP
The application of compressive sensing (CS) to structural health monitoring is an emerging research topic. The basic idea in CS is to use a specially-designed wireless sensor to sample signals that are sparse in some basis (e.g. wavelet basis) directly in a compressed form, and then to reconstruct (decompress) these si…
Compressed particle methods for expensive models with application in Astronomy and Remote Sensingcs.CE
Novel method reduces costly model evaluations in inference problems.
problem Efficiently approximating complex, costly model integrals.
method Compressed Monte Carlo (CMC) scheme for selecting model evaluations.
result Empirical evidence of method's performance in astronomy and remote sensing.
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, al…
Compressed sensing improves MRI scans with data-driven learning.
problem Challenges in applying compressed sensing from research to clinical practice.
method Data-driven learning to address challenges of hand-crafted priors, tuning parameters, and long reconstruction times.
result Compressed sensing can have greater clinical impact with data-driven learning.
Bayesian method improves dictionary learning for complex problems.
problem Efficiently identifying relevant dictionary entries for complex inverse problems.
method Bayesian group sparsity coding and deflation steps to compress and identify relevant subdictionaries.
result Significant computational complexity reduction and improved glitch detection in LIGO experiment.
Review of image compressive sensing algorithms for beginners.
problem Efficiently processing images with limited data.
method Comprehensive review of Total variation methods and other algorithms.
result Standardized comparison of algorithms for compressive sensing applications.
New algorithms allow multiple robots to search efficiently without central coordination.
problem Efficiently locating targets in an unknown environment with multiple robots.
method SPATS and LATSI algorithms for asynchronous multi-agent active search.
result Multiple robots can independently make data-collection decisions without a central coordinator.
This paper proposes a simple adaptive sensing and group testing algorithm for sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and Search (CASS), is shown to be near-optimal in that it succeeds at the lowest possible signal-to-noise-ratio (SNR) levels, improving on previous work in adaptive comp…
We study linear models under heavy-tailed priors from a probabilistic viewpoint. Instead of computing a single sparse most probable (MAP) solution as in standard deterministic approaches, the focus in the Bayesian compressed sensing framework shifts towards capturing the full posterior distribution on the latent variab…
Sparse Polynomial Chaos expansions improve accuracy and efficiency in simulations.
problem Challenges in computational efficiency and accuracy for Polynomial Chaos modeling.
method Sparse Bayesian learning using Variational Relevance Vector Machines.
result Sparse Polynomial Chaos expansions achieve comparable performance to compressive sensing with fewer data points.
Autoencoders learn compressed representations via mutual information maximization.
problem Learning efficient compressed representations of high-dimensional data.
method Proposes Uncertainty Autoencoders that treat latent representations as noisy projections and optimize mutual information.
result 32% improvement in statistical compressed sensing of high-dimensional datasets.
Deep learning produces efficient ternary projections for image compression.
problem Efficiently compress and reconstruct sparse signals from incomplete measurements.
method End-to-end deep learning architecture for learning projection matrices and reconstruction operators.
result Deep learning approach yields more efficient ternary projections compared to state-of-the-art methods.
Study 1-bit compressive sensing with generative models, improving recovery accuracy.
problem Accurately recover sparse vectors from binary measurements with generative models.
method Analyzes noiseless and noisy 1-bit measurements with i.i.d.~Gaussian and Lipschitz continuous generative priors, proving sample complexity bounds and stability properties.
result Proves sample complexity bounds and stability properties for 1-bit compressive sensing with generative models.
Sparse-Gen uses generative models to improve compressed sensing with full signal recovery.
problem Recovering signals with fewer measurements than traditional methods allow.
method Sparse-Gen framework that allows for sparse deviations from the support set.
result Achieves full signal recovery over the full space of signals, not just the support.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priorscs.LG
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
problem Accurately recovering vectors from 1-bit measurements using structured matrices.
method Correlation-based optimization with randomly signed partial Gaussian circulant matrices and generative models.
result Recovery guarantees match those for i.i.d. Gaussian matrices but with faster computation.
Sharp asymptotics derived for phase retrieval and compressed sensing with random generative priors.
problem Phase retrieval and compressed sensing with random measurement matrices.
method Sharp asymptotics derived for optimal performance and polynomial algorithm for random generative priors.
result Compressed phase retrieval becomes tractable with random generative priors, unlike sparse priors.
New algorithm speeds up cluster-based compressive sensing tasks.
problem Efficiently solving multiple compressive sensing tasks with shared information.
method Combines Monte Carlo sampling with iterative linear solvers to avoid explicit covariance matrix computation.
result Up to thousands of times faster and orders of magnitude more memory-efficient compared to existing methods.
This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measur…
Proposes using equivariant generative models for compressed sensing with unknown orientations.
problem Recovering signals with unknown orientations from underdetermined systems of linear measurements.
method Equivariant variational autoencoder as a generative prior for compressed sensing.
result Signals with unknown orientations can be recovered using iterative gradient descent on the latent space of equivariant models.
Diffusion Generative Models Meet Compressed Sensing, with Applications to Imaging and Financestat.ML
CSDM integrates compressed sensing into diffusion models for faster data generation.
problem Efficiently generating synthetic data in high-dimensional spaces.
method Integrating compressed sensing into diffusion models (CSDM) to reduce dimensionality and accelerate inference.
result Achieves provably faster convergence and better latent space dimension selection.
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
Improves convergence speed in compressive sensing with a new probabilistic approach.
problem Efficiently solving the best subset selection problem in compressive sensing.
method Smooth probabilistic reformulation of regularized regression.
result Empirically outperforms existing compressive sensing algorithms across various settings.
The paper provides theoretical guarantees for optimized sampling in compressed sensing, showing error vanishes with more measurements.
problem Theoretical and practical improvements in compressed sensing with optimized sampling schemes.
method Theoretical analysis and empirical experiments with optimized sampling schemes for subsampled unitary matrices.
result The error caused by measurement noise vanishes with an increasing number of measurements for optimized sampling schemes, assuming Gaussian noise.
Paper improves compressed sensing with prior probability information.
problem Enhancing compressed sensing accuracy with prior information.
method Designing a sensing matrix and sparse recovery algorithm using probability-based prior information.
result Proposed methods outperform existing CS systems in simulations.
Optimized sampling scheme for compressed sensing combining randomness and determinism.
problem Improving compressed sensing performance with deterministic sampling.
method Optimized sampling scheme combining random and deterministic selection of rows.
result Measurable improvements in image compressed sensing for generative and sparse priors.
Paper proposes a compression principle for neural networks using Bayesian optimization.
problem Finding methods for making generalizable predictions in machine learning.
method Compression principle and Bayesian optimization approach.
result Optimal predictive models minimize total compressed message length of data and model definition.
New method speeds up sparse Bayesian learning without covariance matrix.
problem Sparse coding problem with uncertainty quantification.
method Covariance-free expectation maximization (CoFEM) that avoids explicit covariance matrix computation.
result Up to thousands of times faster than existing methods without sacrificing accuracy.
Kinetic Compressive Sensingphysics.med-ph
KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
Generative Adversarial Networks improve compressed sensing for task-specific reconstruction.
problem Improving compressed sensing for specific tasks using neural networks.
method Task-aware training of Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems.
result GANs can generate input features for general inference tasks and improve reconstruction and classification performance.
A hybrid framework reduces ML complexity on edge devices.
problem Limited memory and energy on edge devices.
method Compressed data collection and tailored deep learning network.
result Significant reduction in computational complexity and memory.
AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.
problem Binary classification in robust one-bit compressed sensing with adversarial errors.
method AdaBoost and max--margin-classifier approach, with convergence rates improved under certain feature conditions.
result Improved convergence rates and explanation for harmless interpolating adversarial noise.
Improved image reconstruction from sparse measurements using generative models.
problem Signal recovery from limited compressed measurements.
method Generative model with constrained latent variables for stable signal reconstruction.
result Improved reconstruction accuracy and preservation of realistic features.