Showing 1-25 of 4,494 resultsfor "Index Terms":sparse regression
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Learning Sparse Linear Regression Based on Sparse Graph Codes and LT Codes
2020 IEEE 6th International Conference on Computer and Communications (ICCC)
Year: 2020 | Conference Paper |
Sparse linear regression is closely related to sparse recovery and compressed sensing problems. The goal is to stably obtain and reconstruct the original high dimensional K -sparse signal X ECN from the noiseless finite-dimensional linear measurement Y E CM (K <; M <; N). In this paper, we propose a new design framework to effectively solve the parameter estimation problem in sparse linear regress...Show More
Sparse linear regression with elastic net regularization for brain-computer interfaces
John W. Kelly;Alan D. Degenhart;Daniel P. Siewiorek;Asim Smailagic;Wei Wang
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2012 | Conference Paper |
Cited by: Papers (7)
This paper demonstrates the feasibility of decoding neuronal population signals using a sparse linear regression model with an elastic net penalty. In offline analysis of real electrocorticographic (ECoG) neural data the elastic net achieved a timepoint decoding accuracy of 95% for classifying hand grasps vs. rest, and 82% for moving a cursor in 1-D space towards a target. These results were super...Show More
Conditioning of Random Block Subdictionaries With Applications to Block-Sparse Recovery and Regression
The linear model, in which a set of observations is assumed to be given by a linear combination of columns of a matrix (often termed a dictionary), has long been the mainstay of the statistics and signal processing literature. One particular challenge for inference under linear models is understanding the conditions on the dictionary under which reliable inference is possible. This challenge has a...Show More
Sparse multivariate factor regression
2016 IEEE Statistical Signal Processing Workshop (SSP)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
We introduce a sparse multivariate regression algorithm which simultaneously performs dimensionality reduction and parameter estimation. We decompose the coefficient matrix into two sparse matrices: a long matrix mapping the predictors to a set of factors and a wide matrix estimating the responses from the factors. We impose an elastic net penalty on the former and an ℓ1 penalty on the latter. Our...Show More
Algorithms for robust linear regression by exploiting the connection to sparse signal recovery
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (43)
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly model the measurement noise as a combination of two terms; the first term accounts for regular measurement noise modeled as zero mean Gaussian noise, and the second term captures the impact of outliers. The fact that the ...Show More
Reducing Complexity of Echo State Networks with Sparse Linear Regression Algorithms
2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation
Year: 2014 | Conference Paper |
Cited by: Papers (6)
In this paper the use of sparse linear regression algorithms in echo state networks (ESN) is presented for reducing the number of readouts and improving the robustness and generalization properties of ESNs. Three data sets with overall 80 tests are used to validate the use of sparse linear regression algorithms for echo state networks. It is shown that it is possible to increase accuracy on the te...Show More
Sparse linear regression with beta process priors
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (15)
A Bayesian approximation to finding the minimum ℓ0 norm solution for an underdetermined linear system is proposed that is based on the beta process prior. The beta process linear regression (BP-LR) model finds sparse solutions to the underdetermined model y = Φx + ɛ, by modeling the vector x as an element-wise product of a non-sparse weight vector, w, and a sparse binary vector, z, that is drawn f...Show More
Relative Cost Based Model Selection for Sparse High-Dimensional Linear Regression Models
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (4)
In this paper, we propose a novel model selection method named multi-beta-test (MBT) for the sparse high-dimensional linear regression model. The estimation of the correct subset in the linear regression problem is formulated as a series of hypothesis tests where the test statistic is based on the relative least-squares cost of successive parameter models. The performance of MBT is compared to exi...Show More
Cost-sensitive sparse linear regression for crowd counting with imbalanced training data
2016 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2016 | Conference Paper |
Cited by: Papers (4)
Video-based crowd counting (VCC) is a high demanded technique in many video applications. Existing supervised VCC methods essentially learn an intrinsic mapping function between image features and corresponding crowd counts. However, imbalanced training dataset degrades the performance of VCC significantly. Encouraged by recent success in cost-sensitive learning for image classification with imbal...Show More
Sparse linear regression in unions of bases via Bayesian variable selection
In this letter, we propose an approach for sparse linear regression in unions of bases inspired by Bayesian variable selection. Conditionally upon an indicator variable that is 0 or 1, one expansion coefficient of the signal corresponding to one atom of the dictionary is either set to zero or given a Student t prior. A Gibbs sampler (a standard Markov chain Monte Carlo technique) is used to sample...Show More
Two-Way Sparse Reduced-Rank Regression via Scaled Gradient Descent with Hard Thresholding
2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM)
Year: 2024 | Conference Paper |
This paper addresses the problem of two-way sparse reduced-rank regression (TSRRR), which aims to estimate a coefficient matrix that is both low-rank and two-way sparse (sparse in both rows and columns) within a multiple response linear regression model. We formulate TSRRR as a nonconvex optimization problem and propose an efficient, scalable iterative algorithm called Scaled Gradient Descent with...Show More
Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression vectors for each classifier and integrates them in formulating the regression function. However, SMR does not consider the local geometry of image samples, and it assumes that the training samples should e...Show More
MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression
Marian-Daniel Iordache;José M. Bioucas-Dias;Antonio Plaza;Ben Somers
Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures ass...Show More
Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression
In this paper, a novel multi-view facial expression recognition method is presented. Different from most of the facial expression methods that use one view of facial feature vectors in the expression recognition, we synthesize multi-view facial feature vectors and combine them to this goal. In the facial feature extraction, we use the grids with multi-scale sizes to partition each facial image int...Show More
Semi-supervised hyperspectral band selection via sparse linear regression and hypergraph models
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS
Year: 2013 | Conference Paper |
Cited by: Papers (11)
Band selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both labeled and unlabeled samples. These coefficien...Show More
Dual Sparse Constrained Cascade Regression for Robust Face Alignment
Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem continues to be challenging due to the large variability in expression, illumination, pose, and the existence of occlusions in real-world face images. In this paper, we present a dual sparse constrained cascade regression model for robust face alignment. Instead of using the least-squares method during...Show More
Structured Sparsity Characteristics in Hyperspectral Unmixing
2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC)
Year: 2018 | Conference Paper |
Mixed pixels of hyperspectral image own very high sparsity if they are linearly represented by endmembers of aprior spectral library. Hence the sparse regression framework has been introduced to solve the linear spectral unmixing problem and produced sparse fractional abundances. But because of the high coherence of spectral library and noise disturbance, the solution could not be as sparse as rea...Show More
Low-Rank Linear Embedding for Image Recognition
Yudong Chen;Zhihui Lai;Wai Keung Wong;Linlin Shen;Qinghua Hu
IEEE Transactions on Multimedia
Cited by: Papers (23)
Locality preserving projections (LPP) has been widely studied and extended in recent years, because of its promising performance in feature extraction. In this paper, we propose a modified version of the LPP by constructing a novel regression model. To improve the performance of the model, we impose a low-rank constraint on the regression matrix to discover the latent relations between different n...Show More
New Improved Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Extended Bayesian information criterion (EBIC) and extended Fisher information criterion (EFIC) are two popular criteria for model selection in sparse high-dimensional linear regression models. However, EBIC is inconsistent in scenarios when the signal-to-noise-ratio (SNR) is high but the sample size is small, and EFIC is not invariant to data scaling, which affects its performance under different...Show More
EEG signal analysis using sparse approximations
2017 Third International Conference on Biosignals, Images and Instrumentation (ICBSII)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Electroencephalogram (EEG) is physiological signal generated in the brain. Electroencephalography is a method to record the electrical activity of the brain in order to detect the abnormalities of the brain. However, EEG also detects the signals which are not originated from the brain called artifacts. This paper deals with the analysis and extraction of EEG signals in sparse representation using ...Show More
Wavelet-Based Sparse Reduced-Rank Regression for Hyperspectral Image Restoration
In this paper, a method called wavelet-based sparse reduced-rank regression (WSRRR) is proposed for hyperspectral image restoration. The method is based on minimizing a sparse regularization problem subject to an orthogonality constraint. A cyclic descent-type algorithm is derived for solving the minimization problem. For selecting the tuning parameters, we propose a method based on Stein's unbias...Show More
Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model
A novel superpixel-based discriminative sparse model (SBDSM) for spectral-spatial classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is considered as a small spatial region whose size and shape can be adaptively adjusted for different spatial structures. In the proposed approach, the SBDSM first clusters the HSI into many superpixels using an efficient oversegme...Show More
Regression and sparse regression methods for viscosity estimation of acid milk from it's SLS features
Sara Sharifzadeh;Jacob L. Skytte;Otto H. A. Nielsen;Bjarne Ersbøll;Line H. Clemmensen
2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP)
Year: 2012 | Conference Paper |
Cited by: Papers (2)
Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From this investigation, we propose the optimal solution for regression estimation in case of noisy and incon...Show More
On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
Sparse recovery and subset selection are fundamental problems in varied communities, including signal processing, statistics and machine learning. Herein, we focus on an important greedy algorithm for these problems: Backward Stepwise Regression. We present novel guarantees for the algorithm, propose an efficient, numerically stable implementation, and put forth Stepwise Regression with Replacemen...Show More
Multi-linear regression coefficient classifier for recognition
2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
In this paper, a new classifier, called multiple linear regression coefficients (MLRC), is proposed for image recognition. Linear regression classification (LRC) uses the linear combination of the class-model for classification. Sparse representation based classification (SRC) utilizes the globalmodel for classification. Based on the global-concept of SRC, mean representation classification (MRC) ...Show More
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