Relevant bibliographies by topics / Sparse data

Academic literature on the topic 'Sparse data'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Sparse data"

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Aarons, L. "Sparse data analysis." European Journal of Drug Metabolism and Pharmacokinetics 18, no. 1 (March 1993): 97–100. http://dx.doi.org/10.1007/bf03220012.

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Nie, Pengli, Guangquan Xu, Litao Jiao, Shaoying Liu, Jian Liu, Weizhi Meng, Hongyue Wu, et al. "Sparse Trust Data Mining." IEEE Transactions on Information Forensics and Security 16 (2021): 4559–73. http://dx.doi.org/10.1109/tifs.2021.3109412.

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Shepperd, M., and M. Cartwright. "Predicting with sparse data." IEEE Transactions on Software Engineering 27, no. 11 (2001): 987–98. http://dx.doi.org/10.1109/32.965339.

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Wilcosky, T. "Analysis of sparse data." Journal of Clinical Epidemiology 43, no. 8 (January 1990): 755–56. http://dx.doi.org/10.1016/0895-4356(90)90234-g.

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Conroy, John M., Steven G. Kratzer, Robert F. Lucas, and Aaron E. Naiman. "Data-Parallel Sparse Factorization." SIAM Journal on Scientific Computing 19, no. 2 (March 1998): 584–604. http://dx.doi.org/10.1137/s1064827594276412.

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Yao, Fang, Hans-Georg Müller, and Jane-Ling Wang. "Functional Data Analysis for Sparse Longitudinal Data." Journal of the American Statistical Association 100, no. 470 (June 2005): 577–90. http://dx.doi.org/10.1198/016214504000001745.

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Iordache, Marian-Daniel, José M. Bioucas-Dias, and Antonio Plaza. "Sparse Unmixing of Hyperspectral Data." IEEE Transactions on Geoscience and Remote Sensing 49, no. 6 (June 2011): 2014–39. http://dx.doi.org/10.1109/tgrs.2010.2098413.

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McDonald, Mark, Kais Zaman, and Sankaran Mahadevan. "Probabilistic Analysis with Sparse Data." AIAA Journal 51, no. 2 (February 2013): 281–90. http://dx.doi.org/10.2514/1.j050337.

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Ye, Jieping, and Jun Liu. "Sparse methods for biomedical data." ACM SIGKDD Explorations Newsletter 14, no. 1 (December 10, 2012): 4–15. http://dx.doi.org/10.1145/2408736.2408739.

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Hall, Peter, and D. M. Titterington. "On Smoothing Sparse Multinomial Data." Australian Journal of Statistics 29, no. 1 (April 1987): 19–37. http://dx.doi.org/10.1111/j.1467-842x.1987.tb00717.x.

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Dissertations / Theses on the topic "Sparse data"

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Gullipalli, Deep Kumar. "Data envelopment analysis with sparse data." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/13092.

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Master of Science
Department of Industrial & Manufacturing Systems Engineering
David H. Ben-Arieh
Quest for continuous improvement among the organizations and issue of missing data for data analysis are never ending. This thesis brings these two topics under one roof, i.e., to evaluate the productivity of organizations with sparse data. This study focuses on Data Envelopment Analysis (DEA) to determine the efficiency of 41 member clinics of Kansas Association of Medically Underserved (KAMU) with missing data. The primary focus of this thesis is to develop new reliable methods to determine the missing values and to execute DEA. DEA is a linear programming methodology to evaluate relative technical efficiency of homogenous Decision Making Units, using multiple inputs and outputs. Effectiveness of DEA depends on the quality and quantity of data being used. DEA outcomes are susceptible to missing data, thus, creating a need to supplement sparse data in a reliable manner. Determining missing values more precisely improves the robustness of DEA methodology. Three methods to determine the missing values are proposed in this thesis based on three different platforms. First method named as Average Ratio Method (ARM) uses average value, of all the ratios between two variables. Second method is based on a modified Fuzzy C-Means Clustering algorithm, which can handle missing data. The issues associated with this clustering algorithm are resolved to improve its effectiveness. Third method is based on interval approach. Missing values are replaced by interval ranges estimated by experts. Crisp efficiency scores are identified in similar lines to how DEA determines efficiency scores using the best set of weights. There exists no unique way to evaluate the effectiveness of these methods. Effectiveness of these methods is tested by choosing a complete dataset and assuming varying levels of data as missing. Best set of recovered missing values, based on the above methods, serves as a source to execute DEA. Results show that the DEA efficiency scores generated with recovered values are close within close proximity to the actual efficiency scores that would be generated with the complete data. As a summary, this thesis provides an effective and practical approach for replacing missing values needed for DEA.
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Maiga, Aïssata, and Johanna Löv. "Real versus Simulated data for Image Reconstruction : A comparison between training with sparse simulated data and sparse real data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302028.

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Our study investigates how training with sparse simulated data versus sparse real data affects image reconstruction. We compared on several criteria such as number of events, speed and high dynamic range, HDR. The results indicate that the difference between simulated data and real data is not large. Training with real data performed often better, but only by 2%. The findings confirm what earlier studies have shown; training with simulated data generalises well, even when training on sparse datasets as this study shows.
Vår studie undersöker hur träning med gles simulerad data och gles verklig data från en eventkamera, påverkar bildrekonstruktion. Vi tränade två modeller, en med simulerad data och en med verklig för att sedan jämföra dessa på ett flertal kriterier som antal event, hastighet och high dynamic range, HDR. Resultaten visar att skillnaden mellan att träna med simulerad data och verklig data inte är stor. Modellen tränad med verklig data presterade bättre i de flesta fall, men den genomsnittliga skillnaden mellan resultaten är bara 2%. Resultaten bekräftar vad tidigare studier har visat; träning med simulerad data generaliserar bra, och som denna studie visar även vid träning på glesa datamängder.
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Lari, Kamran A. "Sparse data estimation for knowledge processes." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86073.

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During recent years, industry has increasingly focused on knowledge processes. Similar to traditional or manufacturing processes, knowledge processes need to be managed and controlled in order to provide the expected results for which they were designed. During the last decade, the principals of process management have evolved, especially through work done in software engineering and workflow management.
Process monitoring is one of the major components for any process management system. There have been efforts to design process control and monitoring systems; however, no integrated system has yet been developed as a "generic intelligent system shell". In this dissertation, an architecture for an integrated process monitoring system (IPMS) is developed, whereby the end-to-end activities of a process can be automatically measured and evaluated. In order to achieve this goal, various components of the IPMS and the interrelationship among these components are designed.
Furthermore, a comprehensive study on the available methodologies and techniques revealed that sparse data estimation (SDE) is the key component of the IPMS which does not yet exist. Consequently, a series of algorithms and methodologies are developed as the basis for the sparse data estimation of knowledge based processes. Finally, a series of computer programs demonstrate the feasibility and functionality of the proposed approach when applied to a sample process. The sparse data estimation method is successful for not only knowledge based processes, but also for any process, and indeed for any set of activities that can be modeled as a network.
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Beresford, D. J. "3D face modelling from sparse data." Thesis, University of Surrey, 2004. http://epubs.surrey.ac.uk/736/.

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Rommedahl, David, and Martin Lindström. "Learning Sparse Graphs for Data Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295623.

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Graph structures can often be used to describecomplex data sets. In many applications, the graph structureis not known but must be inferred from data. Furthermore, realworld data is often naturally described by sparse graphs. Inthis project, we have aimed at recreating the results describedin previous work, namely to learn a graph that can be usedfor prediction using an ℓ1-penalised LASSO approach. We alsopropose different methods for learning and evaluating the graph. We have evaluated the methods on synthetic data and real-worldSwedish temperature data. The results show that we are unableto recreate the results of the previous research team, but wemanage to learn sparse graphs that could be used for prediction. Further work is needed to verify our results.
Grafstrukturer kan ofta användas för att beskriva komplex data. I många tillämpningar är grafstrukturen inte känd, utan måste läras från data. Vidare beskrivs verklig data ofta naturligt av glesa grafer. I detta projekt har vi försökt återskapa resultaten från ett tidigare forskningsarbete, nämligen att lära en graf som kan användas för prediktion med en ℓ1pennaliserad LASSO-metod. Vi föreslår även andra metoder för inlärning och utvärdering av grafen. Vi har testat metoderna  på syntetisk data och verklig temperaturdata från Sverige.  Resultaten visar att vi inte kan återskapa de tidigare forskarnas resultat, men vi lyckas lära in glesa grafer som kan användas för prediktion. Ytterligare arbete krävs för att verifiera våra resultat.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Prost, Vincent. "Sparse unsupervised learning for metagenomic data." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL013.

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Les avancées technologiques dans le séquençage ADN haut débit ont permis à la métagénomique de considérablement se développer lors de la dernière décennie. Le séquencage des espèces directement dans leur milieu naturel a ouvert de nouveaux horizons dans de nombreux domaines de recherche. La réduction des coûts associée à l'augmentation du débit fait que de plus en plus d'études sont lancées actuellement.Dans cette thèse nous considérons deux problèmes ardus en métagénomique, à savoir le clustering de lectures brutes et l'inférence de réseaux microbiens. Pour résoudre ces problèmes, nous proposons de mettre en oeuvre des méthodes d'apprentissage non supervisées utilisant le principe de parcimonie, ce qui prend la forme concrète de problèmes d'optimisation avec une pénalisation de norme l1.Dans la première partie de la thèse, on considère le problème intermédiaire du clustering des séquences ADN dans des partitions biologiquement pertinentes (binning). La plupart des méthodes computationelles n'effectuent le binning qu'après une étape d'assemblage qui est génératrice d'erreurs (avec la création de contigs chimériques) et de pertes d'information. C'est pourquoi nous nous penchons sur le problème du binning sans assemblage préalable. Nous exploitons le signal de co-abondance des espèces au travers des échantillons mesuré via le comptage des k-mers (sous-séquences de taille k) longs. L'utilisation du Local Sensitive Hashing (LSH) permet de contenir, au coût d'une approximation, l'explosion combinatoire des k-mers possibles dans un espace de cardinal fixé. La première contribution de la thèse est de proposer l'application d'une factorisation en matrices non-négatives creuses (sparse NMF) sur la matrice de comptage des k-mers afin de conjointement extraire une information de variation d'abondance et d'effectuer le clustering des k-mers. Nous montrons d'abord le bien fondé de l'approche au niveau théorique. Puis, nous explorons dans l'état de l'art les méthodes de sparse NMF les mieux adaptées à notre problème. Les méthodes d'apprentissage de dictionnaire en ligne ont particulièrement retenu notre attention de par leur capacité à passer à l'échelle pour des jeux de données comportant un très grand nombre de points. La validation des méthodes de binning en métagénomique sur des données réelles étant difficile à cause de l'absence de vérité terrain, nous avons créé et utilisé plusieurs jeux de données synthétiques pour l'évaluation des différentes méthodes. Nous montrons que l'application de la sparse NMF améliore les méthodes de l'état de l'art pour le binning sur ces jeux de données. Des expérience sur des données métagénomiques réelles issus de 1135 échantillons de microbiotes intestinaux d'individus sains ont également été menées afin de montrer la pertinence de l'approche.Dans la seconde partie de la thèse, on considère les données métagénomiques après le profilage taxonomique, c'est à dire des donnés multivariées représentant les niveaux d'abondance des taxons au sein des échantillons. Les microbes vivant en communautés structurées par des interactions écologiques, il est important de pouvoir identifier ces interactions. Nous nous penchons donc sur le problème de l'inférence de réseau d'interactions microbiennes à partir des profils taxonomiques. Ce problème est souvent abordé dans le cadre théorique des modèles graphiques gaussiens (GGM), pour lequel il existe des algorithmes d'inférence puissants tel que le graphical lasso. Mais les méthodes statistiques existantes sont très limitées par l'aspect extrêmement creux des profils taxonomiques que l'on rencontre en métagénomique, notamment par la grande proportion de zéros dits biologiques (i.e. liés à l'absence réelle de taxons). Nous proposons un model log normal avec inflation de zéro visant à traiter ces zéros biologiques et nous montrons un gain de performance par rapport aux méthodes de l'état de l'art pour l'inférence de réseau d'interactions microbiennes
The development of massively parallel sequencing technologies enables to sequence DNA at high-throughput and low cost, fueling the rise of metagenomics which is the study of complex microbial communities sequenced in their natural environment.Metagenomic problems are usually computationally difficult and are further complicated by the massive amount of data involved.In this thesis we consider two different metagenomics problems: 1. raw reads binning and 2. microbial network inference from taxonomic abundance profiles. We address them using unsupervised machine learning methods leveraging the parsimony principle, typically involving l1 penalized log-likelihood maximization.The assembly of genomes from raw metagenomic datasets is a challenging task akin to assembling a mixture of large puzzles composed of billions or trillions of pieces (DNA sequences). In the first part of this thesis, we consider the related task of clustering sequences into biologically meaningful partitions (binning). Most of the existing computational tools perform binning after read assembly as a pre-processing, which is error-prone (yielding artifacts like chimeric contigs) and discards vast amounts of information in the form of unassembled reads (up to 50% for highly diverse metagenomes). This motivated us to try to address the raw read binning (without prior assembly) problem. We exploit the co-abundance of species across samples as discriminative signal. Abundance is usually measured via the number of occurrences of long k-mers (subsequences of size k). The use of Local Sensitive Hashing (LSH) allows us to contain, at the cost of some approximation, the combinatorial explosion of long k-mers indexing. The first contribution of this thesis is to propose a sparse Non-Negative Matrix factorization (NMF) of the samples x k-mers count matrix in order to extract abundance variation signals. We first show that using sparse NMF is well-grounded since data is a sparse linear mixture of non-negative components. Sparse NMF exploiting online dictionary learning algorithms retained our attention, including its decent behavior on largely asymmetric data matrices. The validation of metagenomic binning being difficult on real datasets, because of the absence of ground truth, we created and used several benchmarks for the different methods evaluated on. We illustrated that sparse NMF improves state of the art binning methods on those datasets. Experiments conducted on a real metagenomic cohort of 1135 human gut microbiota showed the relevance of the approach.In the second part of the thesis, we consider metagenomic data after taxonomic profiling: multivariate data representing abundances of taxa across samples. It is known that microbes live in communities structured by ecological interaction between the members of the community. We focus on the problem of the inference of microbial interaction networks from taxonomic profiles. This problem is frequently cast into the paradigm of Gaussian graphical models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present in this part a zero-inflated log-normal graphical model specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets
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Bissmark, Johan, and Oscar Wärnling. "The Sparse Data Problem Within Classification Algorithms : The Effect of Sparse Data on the Naïve Bayes Algorithm." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209227.

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In today’s society, software and apps based on machine learning and predictive analysis are of the essence. Machine learning has provided us with the possibility of predicting likely future outcomes based on previously collected data in order to save time and resources.   A common problem in machine learning is sparse data, which alters the performance of machine learning algorithms and their ability to calculate accurate predictions. Data is considered sparse when certain expected values in a dataset are missing, which is a common phenomenon in general large scaled data analysis.   This report will mainly focus on the Naïve Bayes classification algorithm and how it is affected by sparse data in comparison to other widely used classification algorithms. The significance of the performance loss associated with sparse data is studied and analyzed, in order to measure the effect sparsity has on the ability to compute accurate predictions.   In conclusion, the results of this report lay a solid argument for the conclusion that the Naïve Bayes algorithm is far less affected by sparse data compared to other common classification algorithms. A conclusion that is in line with what previous research suggests.
I dagens samhälle är maskininlärningsbaserade applikationer och mjukvara, tillsammans med förutsägelser, högst aktuellt. Maskininlärning har gett oss möjligheten att förutsäga troliga utfall baserat på tidigare insamlad data och därigenom spara tid och resurser.   Ett vanligt förekommande problem inom maskininlärning är gles data, eftersom det påverkar prestationen hos algoritmer för maskininlärning och deras förmåga att kunna beräkna precisa förutsägelser. Data anses vara gles när vissa förväntade värden i ett dataset saknas, vilket generellt är vanligt förekommande i storskaliga dataset.   I den här rapporten ligger fokus huvudsakligen på klassificeringsalgoritmen Naïve Bayes och hur den påverkas av gles data jämfört med andra frekvent använda klassifikationsalgoritmer. Omfattningen av prestationssänkningen som resultat av gles data studeras och analyseras för att mäta hur stor effekt gles data har på förmågan att kunna beräkna precisa förutsägelser.   Avslutningsvis lägger resultaten i den här rapporten grund för slutsatsen att algoritmen Naïve Bayes påverkas mindre av gles data jämfört med andra vanligt förekommande klassificeringsalgoritmer. Den här rapportens slutsats stöds även av vad tidigare forskning har visat.
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Embleton, Nina Lois. "Handling sparse spatial data in ecological applications." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5840/.

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Estimating the size of an insect pest population in an agricultural field is an integral part of insect pest monitoring. An abundance estimate can be used to decide if action is needed to bring the population size under control, and accuracy is important in ensuring that the correct decision is made. Conventionally, statistical techniques are used to formulate an estimate from population density data obtained via sampling. This thesis thoroughly investigates an alternative approach of applying numerical integration techniques. We show that when the pest population is spread over the entire field, numerical integration methods provide more accurate results than the statistical counterpart. Meanwhile, when the spatial distribution is more aggregated, the error behaves as a random variable and the conventional error estimates do not hold. We thus present a new probabilistic approach to assessing integration accuracy for such functions, and formulate a mathematically rigorous estimate of the minimum number of sample units required for accurate abundance evaluation in terms of the species diffusion rate. We show that the integration error dominates the error introduced by noise in the density data and thus demonstrate the importance of formulating numerical integration techniques which provide accurate results for sparse spatial data.
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Sjödin, Rickard. "Interpolation and visualization of sparse GPR data." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-170946.

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Ground Penetrating Radar is a tool for mapping the subsurface in a noninvasive way. The radar instrument transmits electromagnetic waves and records the resulting scattered field. Unfortunately, the data from a survey can be hard to interpret, and this holds extra true for non-experts in the field. The data are also usually in 2.5D, or pseudo 3D, meaning that the vast majority of the scanned volume is missing data. Interpolation algorithms can, however, approximate the missing data, and the result can be visualized in an application and in this way ease the interpretation. This report has focused on comparing different interpolation algorithms, with extra focus on behaviour when the data get sparse. The compared methods were: Linear, inverse distance weighting, ordinary kriging, thin plate splines and fk domain zone-pass POCS. They were all found to have some strengths and weaknesses in different aspects, although ordinary kriging was found to be the most accurate and created the least artefacts. Inverse distance weighting performed surprisingly well considering its simplicity and low computational cost. A web-based, easy-to-use visualization application was developed in order to view the results from the interpolations. Some of the tools implemented include time slice, crop of a 3D cube, and iso surface.
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CHERUVU, VINAY KUMAR. "CONTINUOUS ANTEDEPENDENCE MODELS FOR SPARSE LONGITUDINAL DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1315579803.

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Books on the topic "Sparse data"

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M, Erisman A., and Reid John Ker, eds. Direct methods for sparse matrices. Oxford [Oxfordshire]: Clarendon Press, 1986.

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Zlatev, Zahari. Computational methods for general sparse matrices. Dordrecht: Kluwer Academic, 1991.

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Naik, Vijay K. Data traffic reduction schemes for sparse Cholesky factorizations. Hampton, Va: ICASE, 1988.

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Liegmann, Arno. Efficient solution of large sparse linear systems. Kontanz: Hartung-Gorre Verlag, 1995.

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Research Institute for Advanced Computer Science (U.S.), ed. A class of designs for a sparse distributed memory. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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Vanhonacker, Wilfried R. "Combining related and sparse data in linear regression models". Fontainbleau: INSEAD, 1986.

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Vanhonacker, Wilfried R. "Combining related and sparse data in linear regression models". Fontainbleau: INSEAD, 1986.

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Zlatev, Zahari. Computational Methods for General Sparse Matrices. Dordrecht: Springer Netherlands, 1991.

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Research Institute for Advanced Computer Science (U.S.), ed. An alternative design for a sparse distributed memory. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1989.

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Gary, Kumfert, Pothen Alex, and Institute for Computer Applications in Science and Engineering., eds. Object-oriented design for sparse direct solvers. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1999.

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Book chapters on the topic "Sparse data"

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Branham, Richard L. "Sparse Matrices." In Scientific Data Analysis, 34–66. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3362-6_3.

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Shikhman, Vladimir, and David Müller. "Sparse Recovery." In Mathematical Foundations of Big Data Analytics, 131–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62521-7_7.

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Zhao, Haitao, Zhihui Lai, Henry Leung, and Xianyi Zhang. "Sparse Feature Learning." In Information Fusion and Data Science, 103–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40794-0_7.

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Vdovychenko, Ruslan, and Vadim Tulchinsky. "Sparse Distributed Memory for Sparse Distributed Data." In Lecture Notes in Networks and Systems, 74–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16072-1_5.

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Krotkov, Eric Paul. "Modeling Sparse Range Data." In Active Computer Vision by Cooperative Focus and Stereo, 109–22. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9663-5_7.

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Grohs, Philipp. "Optimally Sparse Data Representations." In Harmonic and Applied Analysis, 199–248. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18863-8_5.

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Starck, Jean-Luc. "Sparse Astronomical Data Analysis." In Lecture Notes in Statistics, 239–53. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_23.

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Batra, Shivani, Shelly Sachdeva, Aayushi Bansal, and Suyash Bansal. "Modeling Sparse and Evolving Data." In Big Data Analytics, 204–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_14.

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Adachi, Kohei. "Sparse Regression Analysis." In Matrix-Based Introduction to Multivariate Data Analysis, 341–59. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_21.

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Adachi, Kohei. "Sparse Factor Analysis." In Matrix-Based Introduction to Multivariate Data Analysis, 361–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4103-2_22.

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Conference papers on the topic "Sparse data"

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Saha, Budhaditya, Duc Son Pham, Dinh Phung, and Svetha Venkatesh. "Sparse Subspace Clustering via Group Sparse Coding." In Proceedings of the 2013 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611972832.15.

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Kozera, Ryszard, and Lyle Noakes. "Modelling reduced sparse data." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, edited by Ryszard S. Romaniuk. SPIE, 2016. http://dx.doi.org/10.1117/12.2249260.

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Iskandarov, Islom Z. "Data Structure Sparse Table." In 2023 IEEE XVI International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering (APEIE). IEEE, 2023. http://dx.doi.org/10.1109/apeie59731.2023.10347758.

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Serra, Edoardo, Mikel Joaristi, and Alfredo Cuzzocrea. "Large-scale Sparse Structural Node Representation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377854.

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Asahara, Masato, and Ryohei Fujimaki. "Distributed Bayesian piecewise sparse linear models." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258004.

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Boufounos, Petros, and Richard Baraniuk. "Quantization of Sparse Representations." In 2007 Data Compression Conference (DCC'07). IEEE, 2007. http://dx.doi.org/10.1109/dcc.2007.68.

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Madden, Liam, Stephen Becker, and Emiliano DallrAnese. "Online Sparse Subspace Clustering." In 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755556.

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Hochbaum, Dorit S., and Philipp Baumann. "Sparse computation for large-scale data mining." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004252.

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Chen, Zhong, Huixin Zhan, Victor Sheng, Andrea Edwards, and Kun Zhang. "Proximal Cost-sensitive Sparse Group Online Learning." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021084.

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Pratap, Rameshwar, Raghav Kulkarni, and Ishan Sohony. "Efficient Dimensionality Reduction for Sparse Binary Data." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622338.

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Reports on the topic "Sparse data"

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Tafolla, Tanya, Eappen Nelluvelil, Jacob Moore, Daniel Dunning, Nathaniel Morgan, and Robert Robey. MATAR: Data-Oriented Sparse Data Representation. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1773304.

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Osher, Stanley. Sparse Recovery for Scientific Data. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1561286.

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Quach, Tu-Thach, Sapan Agarwal, Conrad D. James, Matthew J. Marinella, and James Bradley Aimone. Sparse Data Acquisition on Emerging Memory Architectures. Office of Scientific and Technical Information (OSTI), July 2018. http://dx.doi.org/10.2172/1530151.

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Mahmoudi, Mona, and Guillermo Sapiro. Sparse Representations for Three-Dimensional Range Data Restoration. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada513241.

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Lopez, Oscar, Richard Lehoucq, and Daniel Dunlavy. Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1841834.

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Casey, K. F., and B. A. Baertlein. Wideband pulse reconstruction from sparse spectral-amplitude data. Final report. Office of Scientific and Technical Information (OSTI), January 1993. http://dx.doi.org/10.2172/446294.

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Lin, Youzuo, and Lianjie Huang. Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1168704.

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Haile, Mulugeta A. Spatial Compressive Sensing for Strain Data Reconstruction from Sparse Sensors. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada611851.

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Nichols, Jonathan M., Frank Bucholtz, and Joseph V. Michalowicz. Intelligent Data Fusion Using Sparse Representations and Nonlinear Dimensionality Reduction. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada507109.

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Meinshausen, Nicolai, and Bin Yu. Lasso-type recovery of sparse representations for high-dimensional data. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada472998.

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