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Relevant bibliographies by topics / Sparse regression codes

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Academic literature on the topic 'Sparse regression codes'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Sparse regression codes"

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Venkataramanan, Ranmji, Sekhar Tatikonda, and Andrew Barron. "Sparse Regression Codes." Foundations and Trends® in Communications and Information Theory 15, no. 1-2 (2019): 1–195. http://dx.doi.org/10.1561/0100000092.

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Cao, Haiwen, and Pascal O. Vontobel. "Using List Decoding to Improve the Finite-Length Performance of Sparse Regression Codes." IEEE Transactions on Communications 69, no. 7 (2021): 4282–93. http://dx.doi.org/10.1109/tcomm.2021.3071540.

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Ajana, Soufiane, Niyazi Acar, Lionel Bretillon, et al. "Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size." Bioinformatics 35, no. 19 (2019): 3628–34. http://dx.doi.org/10.1093/bioinformatics/btz135.

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Abstract Motivation In some prediction analyses, predictors have a natural grouping structure and selecting predictors accounting for this additional information could be more effective for predicting the outcome accurately. Moreover, in a high dimension low sample size framework, obtaining a good predictive model becomes very challenging. The objective of this work was to investigate the benefits of dimension reduction in penalized regression methods, in terms of prediction performance and variable selection consistency, in high dimension low sample size data. Using two real datasets, we compared the performances of lasso, elastic net, group lasso, sparse group lasso, sparse partial least squares (PLS), group PLS and sparse group PLS. Results Considering dimension reduction in penalized regression methods improved the prediction accuracy. The sparse group PLS reached the lowest prediction error while consistently selecting a few predictors from a single group. Availability and implementation R codes for the prediction methods are freely available at https://github.com/SoufianeAjana/Blisar. Supplementary information Supplementary data are available at Bioinformatics online.

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Venkataramanan, Ramji, and Sekhar Tatikonda. "The Rate-Distortion Function and Excess-Distortion Exponent of Sparse Regression Codes With Optimal Encoding." IEEE Transactions on Information Theory 63, no. 8 (2017): 5228–43. http://dx.doi.org/10.1109/tit.2017.2716360.

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Boulesteix, Anne-Laure, Riccardo De Bin, Xiaoyu Jiang, and Mathias Fuchs. "IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data." Computational and Mathematical Methods in Medicine 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/7691937.

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As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.

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Walker, Tiffany A., Ben Waite, Mark G. Thompson, et al. "Risk of Severe Influenza Among Adults With Chronic Medical Conditions." Journal of Infectious Diseases 221, no. 2 (2019): 183–90. http://dx.doi.org/10.1093/infdis/jiz570.

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Abstract Background Severe influenza illness is presumed more common in adults with chronic medical conditions (CMCs), but evidence is sparse and often combined into broad CMC categories. Methods Residents (aged 18–80 years) of Central and South Auckland hospitalized for World Health Organization-defined severe acute respiratory illness (SARI) (2012–2015) underwent influenza virus polymerase chain reaction testing. The CMC statuses for Auckland residents were modeled using hospitalization International Classification of Diseases, Tenth Revision codes, pharmaceutical claims, and laboratory results. Population-level influenza rates in adults with congestive heart failure (CHF), coronary artery disease (CAD), cerebrovascular accidents (CVA), chronic obstructive pulmonary disease (COPD), asthma, diabetes mellitus (DM), and end-stage renal disease (ESRD) were calculated by Poisson regression stratified by age and adjusted for ethnicity. Results Among 891 276 adults, 2435 influenza-associated SARI hospitalizations occurred. Rates were significantly higher in those with CMCs compared with those without the respective CMC, except for older adults with DM or those aged &lt;65 years with CVA. The largest effects occurred with CHF (incidence rate ratio [IRR] range, 4.84–13.4 across age strata), ESRD (IRR range, 3.30–9.02), CAD (IRR range, 2.77–10.7), and COPD (IRR range, 5.89–8.78) and tapered with age. Conclusions Our findings support the increased risk of severe, laboratory-confirmed influenza disease among adults with specific CMCs compared with those without these conditions.

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Roark, Christopher, David Case, Mark Gritz, et al. "Nationwide analysis of hospital-to-hospital transfer in patients with aneurysmal subarachnoid hemorrhage requiring aneurysm repair." Journal of Neurosurgery 131, no. 4 (2019): 1254–61. http://dx.doi.org/10.3171/2018.4.jns172269.

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OBJECTIVEAneurysmal subarachnoid hemorrhage (aSAH) has devastating consequences. The association between higher institutional volumes and improved outcomes for aSAH patients has been studied extensively. However, the literature exploring patterns of transfer in this context is sparse. Expansion of the endovascular workforce has raised concerns about the decentralization of care, reduced institutional volumes, and worsened patient outcomes. In this paper, the authors explored various patient and hospital factors associated with the transfer of aSAH patients by using a nationally representative database.METHODSThe 2013 and 2014 years of the National Inpatient Sample (NIS) were used to define an observational cohort of patients with ruptured brain aneurysms. The initial search identified patients with SAH (ICD-9-CM 430). Those with concomitant codes suggesting trauma or other intracranial vascular abnormalities were excluded. Finally, the patients who had not undergone a subsequent procedure to repair an intracranial aneurysm were excluded. These criteria yielded a cohort of 4373 patients, 1379 of whom had undergone microsurgical clip ligation and 2994 of whom had undergone endovascular repair. The outcome of interest was transfer status, and the NIS data element TRAN_IN was used to define this state. Multiple explanatory variables were identified, including age, sex, primary payer, median household income by zip code, race, hospital size, hospital control, hospital teaching status, and hospital location. These variables were evaluated using descriptive statistics, bivariate correlation analysis, and multivariable logistic regression modeling to determine their relationship with transfer status.RESULTSPatients with aSAH who were treated in an urban teaching hospital had higher odds of being a transfer (OR 2.15, 95% CI 1.71–2.72) than the patients in urban nonteaching hospitals. White patients were more likely to be transfer patients than were any of the other racial groups (p < 0.0001). Moreover, patients who lived in the highest-income zip codes were less likely to be transferred than the patients in the lowest income quartile (OR 0.78, 95% CI 0.64–0.95). Repair type (clip vs coil) and primary payer were not associated with transfer status.CONCLUSIONSA relatively high percentage of patients with aSAH are transferred between acute care hospitals. Race and income were associated with transfer status. White patients are more likely to be transferred than other races. Patients from zip codes with the highest income transferred at lower rates than those from the lowest income quartile. Transfer patients were preferentially sent to urban teaching hospitals. The modality of aneurysm treatment was not associated with transfer status.

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Yin, Dong, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran. "Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes." IEEE Transactions on Information Theory 65, no. 3 (2019): 1430–51. http://dx.doi.org/10.1109/tit.2018.2864276.

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Barnes, Hayley, Anna-Carin Olin, Kjell Torén, et al. "Occupation versus environmental factors in hypersensitivity pneumonitis: population attributable fraction." ERJ Open Research 6, no. 4 (2020): 00374–2020. http://dx.doi.org/10.1183/23120541.00374-2020.

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BackgroundDespite well-documented case series of hypersensitivity pneumonitis (HP), epidemiological data delineating relative contributions of risk factors are sparse. To address this, we estimated HP risk in a case-referent study of occupational and nonoccupational exposures.MethodsWe recruited cases of HP by ICD-9 codes from an integrated healthcare delivery system (IHCDS) and a tertiary medical care centre. We drew referents, matched for age and sex, from the IHCDS. Participants underwent comprehensive, structured telephone interviews eliciting details of occupational and home environmental exposures. We employed a hierarchical analytic approach for data reduction based on the false discovery rate method within clusters of exposures. We measured lung function and selected biomarkers in a subset of participants. We used multivariate logistic regression to estimate exposure-associated odds ratios (ORs) and population attributable fractions (PAFs) for HP.ResultsWe analysed data for 192 HP cases (148 IHCDS; 44 tertiary care) and 229 referents. Occupational exposures combined more than doubled the odds of developing HP (OR 2.67; 95% CI 1.73–4.14) with a PAF of 34% (95% CI 21–46%); nonoccupational bird exposure also doubled the HP odds (OR 2.02; 95% CI 1.13–3.60), with a PAF of 12% (3–21%). Lung function and selected biomarkers did not substantively modify the risk estimates on the basis of questionnaire data alone.DiscussionIn a case-referent approach evaluating HP risk, identifiable exposures accounted, on an epidemiological basis, for approximately two in three cases of disease; conversely, for one in three, the risk factors for disease remained elusive.

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Wittekindt, Boris, Rolf Schloesser, Nora Doberschuetz, et al. "Epidemiology and Outcome of Major Congenital Malformations in a Large German County." European Journal of Pediatric Surgery 29, no. 03 (2018): 282–89. http://dx.doi.org/10.1055/s-0038-1642630.

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Introduction Congenital malformations are associated with substantial neonatal morbidity and mortality. Furthermore, only sparse data are available on the modalities of care provided to and the associated clinical outcomes in affected neonates. In this study, we focused on five malformations that require surgery during the neonatal period: duodenal stenosis and atresia (DA), gastroschisis (GA), omphalocele (OM), congenital diaphragmatic herniation (CDH), and esophageal atresia (EA). Materials and Methods We reviewed the Hessian neonatal registry (2010–2015) to identify records including the ICD-10 (International Classification of Diseases, Tenth Edition) codes for the aforementioned diagnoses and identified 283 patients who were affected by at least one of these conditions. Multiple regression analyses were performed to further identify risk factors for mortality and extended length of hospital stay. Results The incidence rates per 10,000 live births and inhospital mortality rates were as follows: DA: 1.79 and 3.6%; GA: 1.79 and 1.8%; OM: 1.60 and 24%; CDH: 1.32 and 27.5%; and EA: 2.67 and 11.1%, respectively. Thirty-three percent of the patients had not been born in a perinatal center in which corrective surgeries were performed. The following risk factors were significantly associated with early mortality: trisomy 13 and 18, congenital heart defects, prematurity, and high-risk malformations (OM and CDH). The predictors of length of stay were as follows: gestational age, number of additional malformations, and treatment in the center with the highest patient volume. Conclusion Epidemiology and outcome of major congenital malformations in Hesse, Germany, are comparable to previously published data. In addition, our data revealed a volume–outcome association with regard to the length of hospital stay.

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Dissertations / Theses on the topic "Sparse regression codes"

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Greig, Adam. "Design techniques for efficient sparse regression codes." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274917.

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Sparse regression codes (SPARCs) are a recently introduced coding scheme for the additive white Gaussian noise channel, for which polynomial time decoding algorithms have been proposed which provably achieve the Shannon channel capacity. One such algorithm is the approximate message passing (AMP) decoder. However, directly implementing these decoders does not yield good empirical performance at practical block lengths. This thesis develops techniques for improving both the error rate performance, and the time and memory complexity, of the AMP decoder. It focuses on practical and efficient implementations for both single- and multi-user scenarios. A key design parameter for SPARCs is the power allocation, which is a vector of coefficients which determines how codewords are constructed. In this thesis, novel power allocation schemes are proposed which result in several orders of magnitude improvement to error rate compared to previous designs. Further improvements to error rate come from investigating the role of other SPARC construction parameters, and from performing an online estimation of a key AMP parameter instead of using a pre-computed value. Another significant improvement to error rates comes from a novel three-stage decoder which combines SPARCs with an outer code based on low-density parity-check codes. This construction protects only vulnerable sections of the SPARC codeword with the outer code, minimising the impact to the code rate. The combination provides a sharp waterfall in bit error rates and very low overall codeword error rates. Two changes to the basic SPARC structure are proposed to reduce computational and memory complexity. First, the design matrix is replaced with an efficient in-place transform based on Hadamard matrices, which dramatically reduces the overall decoder time and memory complexity with no impact on error rate. Second, an alternative SPARC design is developed, called Modulated SPARCs. These are shown to also achieve the Shannon channel capacity, while obtaining similar empirical error rates to the original SPARC, and permitting a further reduction in time and memory complexity. Finally, SPARCs are implemented for the broadcast and multiple access channels, and for the multiple description and Wyner-Ziv source coding models. Designs for appropriate power allocations and decoding strategies are proposed and are found to give good empirical results, demonstrating that SPARCs are also well suited to these multi-user settings.

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Book chapters on the topic "Sparse regression codes"

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Chen, Li, and Lala Aicha Coulibaly. "Data Science and Big Data Practice Using Apache Spark and Python." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4963-6.ch004.

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Data science and big data analytics are still at the center of computer science and information technology. Students and researchers not in computer science often found difficulties in real data analytics using programming languages such as Python and Scala, especially when they attempt to use Apache-Spark in cloud computing environments-Spark Scala and PySpark. At the same time, students in information technology could find it difficult to deal with the mathematical background of data science algorithms. To overcome these difficulties, this chapter will provide a practical guideline to different users in this area. The authors cover the main algorithms for data science and machine learning including principal component analysis (PCA), support vector machine (SVM), k-means, k-nearest neighbors (kNN), regression, neural networks, and decision trees. A brief description of these algorithms will be explained, and the related code will be selected to fit simple data sets and real data sets. Some visualization methods including 2D and 3D displays will be also presented in this chapter.

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Conference papers on the topic "Sparse regression codes"

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Hsieh, Kuan, and Ramji Venkataramanan. "Modulated Sparse Regression Codes." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174140.

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Zhou, Huaqiao, Xiang Zheng, Weijun Zeng, and Mengxue Deng. "Learning Sparse Linear Regression Based on Sparse Graph Codes and LT Codes." In 2020 IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc51575.2020.9345313.

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Rush, Cynthia, Kuan Hsieh, and Ramji Venkataramanan. "Capacity-achieving sparse regression codes via spatial coupling." In 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613392.

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Venkataramanan, Ramji, and Sekhar Tatikonda. "Sparse Regression codes: Recent results and future directions." In 2013 IEEE Information Theory Workshop (ITW 2013). IEEE, 2013. http://dx.doi.org/10.1109/itw.2013.6691313.

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Venkataramanan, Ramji, and Sekhar Tatikonda. "Sparse regression codes for multi-terminal source and channel coding." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483463.

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Rush, Cynthia, Kuan Hsieh, and Ramji Venkataramanan. "Spatially Coupled Sparse Regression Codes with Sliding Window AMP Decoding." In 2019 IEEE Information Theory Workshop (ITW). IEEE, 2019. http://dx.doi.org/10.1109/itw44776.2019.8989108.

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Fengler, Alexander, Peter Jung, and Giuseppe Caire. "Unsourced Multiuser Sparse Regression Codes achieve the Symmetric MAC Capacity." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174035.

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Rush, Cynthia, and Ramji Venkataramanan. "The error exponent of sparse regression codes with AMP decoding." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006975.

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Hsieh, Kuan, Cynthia Rush, and Ramji Venkataramanan. "Spatially Coupled Sparse Regression Codes: Design and State Evolution Analysis." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437615.

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Rush, Cynthia, Adam Greig, and Ramji Venkataramanan. "Capacity-achieving Sparse Regression Codes via approximate message passing decoding." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282809.

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