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Journal articles on the topic 'Sparse data'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Uitert, Miranda van, Wouter Meuleman, and Lodewyk Wessels. "Biclustering Sparse Binary Genomic Data." Journal of Computational Biology 15, no. 10 (December 2008): 1329–45. http://dx.doi.org/10.1089/cmb.2008.0066.

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12

Tan, Kean Ming, and Daniela M. Witten. "Sparse Biclustering of Transposable Data." Journal of Computational and Graphical Statistics 23, no. 4 (October 2, 2014): 985–1008. http://dx.doi.org/10.1080/10618600.2013.852554.

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13

Floriello, Davide, and Valeria Vitelli. "Sparse clustering of functional data." Journal of Multivariate Analysis 154 (February 2017): 1–18. http://dx.doi.org/10.1016/j.jmva.2016.10.008.

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14

Tegtmeier, S., A. Gisolf, and D. J. Verschuur. "3D sparse-data Kirchhoff redatuming." Geophysical Prospecting 52, no. 6 (November 2004): 509–21. http://dx.doi.org/10.1111/j.1365-2478.2004.00443.x.

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15

Liese, Friedrich. "Selection procedures for sparse data." Journal of Statistical Planning and Inference 136, no. 7 (July 2006): 2035–52. http://dx.doi.org/10.1016/j.jspi.2005.08.037.

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16

Djurcilov, S., and A. Pang. "Visualizing sparse gridded data sets." IEEE Computer Graphics and Applications 20, no. 5 (2000): 52–57. http://dx.doi.org/10.1109/38.865880.

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17

Trendafilov, Nickolay, Martin Kleinsteuber, and Hui Zou. "Sparse matrices in data analysis." Computational Statistics 29, no. 3-4 (December 24, 2013): 403–5. http://dx.doi.org/10.1007/s00180-013-0468-8.

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18

Garcke, J., M. Griebel, and M. Thess. "Data Mining with Sparse Grids." Computing 67, no. 3 (October 1, 2001): 225–53. http://dx.doi.org/10.1007/s006070170007.

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19

Gao, Zhi, Mingjie Lao, Yongsheng Sang, Fei Wen, Bharath Ramesh, and Ruifang Zhai. "Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint." Sensors 18, no. 5 (May 6, 2018): 1449. http://dx.doi.org/10.3390/s18051449.

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20

Nazari Siahsar, Mohammad Amir, Saman Gholtashi, Amin Roshandel Kahoo, Wei Chen, and Yangkang Chen. "Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data." GEOPHYSICS 82, no. 6 (November 1, 2017): V385—V396. http://dx.doi.org/10.1190/geo2017-0084.1.

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Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing a common sparse coefficient matrix for the whole related tasks of the data. Basically, in the learning process, each task can help other tasks to learn better and thus a sparser representation is obtained. Furthermore, different from other DL methods that use a limited random number of patches to learn a dictionary, the proposed algorithm can take the whole data information into account with a reasonable time cost and thus can obtain an efficient and effective denoising performance. We have applied the method on synthetic and real 3D data, which demonstrated superior performance in random noise attenuation when compared with state-of-the-art denoising methods such as MSSA, BM4D, and FXY predictive filtering, especially in amplitude and continuity preservation in low signal-to-noise ratio cases and fault zones.

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21

Dousti Mousavi, Niloufar, Jie Yang, and Hani Aldirawi. "Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data." Genes 14, no. 2 (February 3, 2023): 403. http://dx.doi.org/10.3390/genes14020403.

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Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as 83% of the model variability.

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22

Lorenzo, Hadrien, Olivier Cloarec, Rodolphe Thiébaut, and Jérôme Saracco. "Data‐driven sparse partial least squares." Statistical Analysis and Data Mining: The ASA Data Science Journal 15, no. 2 (October 18, 2021): 264–82. http://dx.doi.org/10.1002/sam.11558.

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23

Fitzgerald, Tesca, Ashok Goel, and Andrea Thomaz. "Abstraction in data-sparse task transfer." Artificial Intelligence 300 (November 2021): 103551. http://dx.doi.org/10.1016/j.artint.2021.103551.

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24

Du, Pang, Yunnan Xu, John Robertson, and Ryan Senger. "Sparse logistic regression on functional data." Statistics and Its Interface 15, no. 2 (2022): 171–79. http://dx.doi.org/10.4310/21-sii688.

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25

Sheu, Ching-Fan, Yuh-Shiow Lee, and Pei-Ying Shih. "Analyzing recognition performance with sparse data." Behavior Research Methods 40, no. 3 (August 2008): 722–27. http://dx.doi.org/10.3758/brm.40.3.722.

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26

Jun, Sung-Hae, Seung-Joo Lee, and Kyung-Whan Oh. "Sparse Data Cleaning using Multiple Imputations." International Journal of Fuzzy Logic and Intelligent Systems 4, no. 1 (June 1, 2004): 119–24. http://dx.doi.org/10.5391/ijfis.2004.4.1.119.

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27

Walker, David A., and Thomas J. Smith. "Logistic Regression Under Sparse Data Conditions." Journal of Modern Applied Statistical Methods 18, no. 2 (September 25, 2020): 2–18. http://dx.doi.org/10.22237/jmasm/1604190660.

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The impact of sparse data conditions was examined among one or more predictor variables in logistic regression and assessed the effectiveness of the Firth (1993) procedure in reducing potential parameter estimation bias. Results indicated sparseness in binary predictors introduces bias that is substantial with small sample sizes, and the Firth procedure can effectively correct this bias.

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28

Rump, Martin, and Reinhard Klein. "Spectralization: Reconstructing spectra from sparse data." Computer Graphics Forum 29, no. 4 (August 26, 2010): 1347–54. http://dx.doi.org/10.1111/j.1467-8659.2010.01730.x.

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29

Hanafy, Sherif M., and Gerard T. Schuster. "Interferometric interpolation of sparse marine data." Geophysical Prospecting 62, no. 1 (October 11, 2013): 1–16. http://dx.doi.org/10.1111/1365-2478.12066.

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30

Branham, Richard L. ,. Jr. "Sparse matrices in astronomical data reduction." Astronomical Journal 104 (October 1992): 1658. http://dx.doi.org/10.1086/116350.

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31

Ikeda, Shiro, Hirokazu Odaka, and Makoto Uemura. "Sparse Modeling for Astronomical Data Analysis." Journal of Physics: Conference Series 699 (March 2016): 012008. http://dx.doi.org/10.1088/1742-6596/699/1/012008.

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32

Kenny, P., G. Boulianne, and P. Dumouchel. "Eigenvoice modeling with sparse training data." IEEE Transactions on Speech and Audio Processing 13, no. 3 (May 2005): 345–54. http://dx.doi.org/10.1109/tsa.2004.840940.

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33

Afroz, Farzana, Matt Parry, and David Fletcher. "Estimating overdispersion in sparse multinomial data." Biometrics 76, no. 3 (December 16, 2019): 834–42. http://dx.doi.org/10.1111/biom.13194.

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34

Jääskinen, Väinö, Jie Xiong, Jukka Corander, and Timo Koski. "Sparse Markov Chains for Sequence Data." Scandinavian Journal of Statistics 41, no. 3 (October 31, 2013): 639–55. http://dx.doi.org/10.1111/sjos.12053.

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35

Hwang, K. L., S. Person, and R. Grimsoo. "DETECTING DISEASE CLUSTERS IN SPARSE DATA." Epidemiology 9, Supplement (July 1998): S102. http://dx.doi.org/10.1097/00001648-199807001-00324.

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36

Tang, Xiangyu, and Jie Zhou. "Dynamic Personalized Recommendation on Sparse Data." IEEE Transactions on Knowledge and Data Engineering 25, no. 12 (December 2013): 2895–99. http://dx.doi.org/10.1109/tkde.2012.229.

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37

Habeck, Michael. "Statistical mechanics analysis of sparse data." Journal of Structural Biology 173, no. 3 (March 2011): 541–48. http://dx.doi.org/10.1016/j.jsb.2010.09.016.

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38

Campos, Manuel, Olivera Francetic, and Michael Nilges. "Modeling pilus structures from sparse data." Journal of Structural Biology 173, no. 3 (March 2011): 436–44. http://dx.doi.org/10.1016/j.jsb.2010.11.015.

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39

Picheny, Victor, Rémi Servien, and Nathalie Villa-Vialaneix. "Interpretable sparse SIR for functional data." Statistics and Computing 29, no. 2 (March 2, 2018): 255–67. http://dx.doi.org/10.1007/s11222-018-9806-6.

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40

Chen, Yangkang, Xiaohong Chen, Yufeng Wang, and Shaohuan Zu. "The Interpolation of Sparse Geophysical Data." Surveys in Geophysics 40, no. 1 (September 27, 2018): 73–105. http://dx.doi.org/10.1007/s10712-018-9501-3.

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41

Zhang, Zhongheng, and Bo Ren. "Estimation bias resulting from sparse data." Intensive Care Medicine 42, no. 11 (September 27, 2016): 1842–43. http://dx.doi.org/10.1007/s00134-016-4495-0.

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42

Partsinevelos, Panayotis, Peggy Agouris, and Anthony Stefanidis. "Reconstructing spatiotemporal trajectories from sparse data." ISPRS Journal of Photogrammetry and Remote Sensing 60, no. 1 (December 2005): 3–16. http://dx.doi.org/10.1016/j.isprsjprs.2005.10.004.

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43

Tautges, Jochen, Arno Zinke, Björn Krüger, Jan Baumann, Andreas Weber, Thomas Helten, Meinard Müller, Hans-Peter Seidel, and Bernd Eberhardt. "Motion reconstruction using sparse accelerometer data." ACM Transactions on Graphics 30, no. 3 (May 2011): 1–12. http://dx.doi.org/10.1145/1966394.1966397.

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44

Fosson, Sophie M., Vito Cerone, and Diego Regruto. "Sparse linear regression from perturbed data." Automatica 122 (December 2020): 109284. http://dx.doi.org/10.1016/j.automatica.2020.109284.

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45

Ebtehaj, Ardeshir M., Milija Zupanski, GILAD Lerman, and Efi Foufoula-Georgiou. "Variational data assimilation via sparse regularisation." Tellus A: Dynamic Meteorology and Oceanography 66, no. 1 (February 11, 2014): 21789. http://dx.doi.org/10.3402/tellusa.v66.21789.

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46

Subbiah, M., B. Kishore Kumar, and M. R. Srinivasan. "Bayesian Approach to Multicentre Sparse Data." Communications in Statistics - Simulation and Computation 37, no. 4 (February 27, 2008): 687–96. http://dx.doi.org/10.1080/03610910701884062.

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47

Mahmoudi, M., and G. Sapiro. "Sparse Representations for Range Data Restoration." IEEE Transactions on Image Processing 21, no. 5 (May 2012): 2909–15. http://dx.doi.org/10.1109/tip.2012.2185940.

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48

Vitek, Olga, Chris Bailey-Kellogg, Bruce Craig, and Jan Vitek. "Inferential backbone assignment for sparse data." Journal of Biomolecular NMR 35, no. 3 (July 2006): 187–208. http://dx.doi.org/10.1007/s10858-006-9027-8.

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49

Schleif, Frank-Michael, Xibin Zhu, and Barbara Hammer. "Sparse conformal prediction for dissimilarity data." Annals of Mathematics and Artificial Intelligence 74, no. 1-2 (January 31, 2014): 95–116. http://dx.doi.org/10.1007/s10472-014-9402-1.

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50

Hilton, A. "Scene modelling from sparse 3D data." Image and Vision Computing 23, no. 10 (September 2005): 900–920. http://dx.doi.org/10.1016/j.imavis.2005.05.018.

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