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Journal articles on the topic 'High dimensional data'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 October 2024

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1

Geethika, Paruchuri, and Voleti Prasanthi. "Booster in High Dimensional Data Classification." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1186–90. http://dx.doi.org/10.31142/ijtsrd11368.

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2

Gayathri, Tata, and N. Durga. "Privacy Preserving Approaches for High Dimensional Data." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 1120–25. http://dx.doi.org/10.31142/ijtsrd2430.

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3

G, Vasanthi. "Nearest Neighbors Search Algorithm for High Dimensional Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (July 30, 2020): 1215–18. http://dx.doi.org/10.5373/jardcs/v12sp8/20202636.

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4

Amaratunga, Dhammika, and Javier Cabrera. "High-dimensional data." Journal of the National Science Foundation of Sri Lanka 44, no. 1 (March 31, 2016): 3. http://dx.doi.org/10.4038/jnsfsr.v44i1.7976.

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5

Geubbelmans, Melvin, Axel-Jan Rousseau, Dirk Valkenborg, and Tomasz Burzykowski. "High-dimensional data." American Journal of Orthodontics and Dentofacial Orthopedics 164, no. 3 (September 2023): 453–56. http://dx.doi.org/10.1016/j.ajodo.2023.06.012.

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6

Yuan, Xupeng, Miao Zhao, Xinjun Guo, Yao Li, Zongsong Gan, and Hao Ruan. "Optical tape for high capacity three-dimensional optical data storage." Chinese Optics Letters 18, no. 1 (2020): 012001. http://dx.doi.org/10.3788/col202018.012001.

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7

Khot, Tejas. "Visualizing high-dimensional data." XRDS: Crossroads, The ACM Magazine for Students 23, no. 2 (December 15, 2016): 66–67. http://dx.doi.org/10.1145/3021604.

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8

Kriegel, Hans-Peter, and Eirini Ntoutsi. "Clustering high dimensional data." ACM SIGKDD Explorations Newsletter 15, no. 2 (June 16, 2014): 1–8. http://dx.doi.org/10.1145/2641190.2641192.

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9

Tang, Lin. "High-dimensional data visualization." Nature Methods 17, no. 2 (February 2020): 129. http://dx.doi.org/10.1038/s41592-020-0750-y.

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10

Kriegel, Hans-Peter, Peer Kröger, and Arthur Zimek. "Clustering high-dimensional data." ACM Transactions on Knowledge Discovery from Data 3, no. 1 (March 2009): 1–58. http://dx.doi.org/10.1145/1497577.1497578.

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11

Vempala, Santosh S. "Modeling high-dimensional data." Communications of the ACM 55, no. 2 (February 2012): 112. http://dx.doi.org/10.1145/2076450.2076473.

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12

Pavithra, M., and R. M. S. Parvathi. "High dimensional data clustering." APTIKOM Journal on Computer Science and Information Technologies 3, no. 1 (March 1, 2018): 21–30. http://dx.doi.org/10.11591/aptikom.j.csit.82.

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Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points. The proposed method called “kernel trick” and “Collective Neighbour Clustering”, which takes as input measures of correspondence between pairs of data points. Real-valued hubs are exchanged between data points until a high-quality set of patterns and corresponding clusters gradually emerges [2]. To validate our theory by demonstrating that hubness is a high-quality measure of point centrality within a high dimensional information cluster, and by proposing several hubness-based clustering algorithms, showing that main hubs can be used effectively as cluster prototypes or as guides during the search for centroid-based cluster patterns [4]. Experimental results demonstrate the good performance of our proposed algorithms in manifold settings, mainly focused on large quantities of overlapping noise. The proposed methods are modified mostly for detecting approximately hyper spherical clusters and need to be extended to properly handle clusters of arbitrary shapes [6]. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself [10]. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reﬂections on implications of our model for future research. High-dimensional data arise naturally in many domains, and have regularly presented a great challenge for traditional data-mining techniques, both in terms of effectiveness and efficiency [7]. Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points. In this paper we take a novel perspective on the problem of clustering high-dimensional data [8]. Instead of attempting to avoid the curse of dimensionality by observing a lower-dimensional feature subspace, we embrace dimensionality by taking advantage of some inherently high-dimensional phenomena. More speciﬁcally, we show that hubness, i.e., the tendency of high-dimensional data to contain points (hubs) that frequently occur in k-nearest neighbour lists of other points, can be successfully exploited in clustering. We validate our hypothesis by proposing several hubness-based clustering algorithms and testing them on high-dimensional data. Experimental results demonstrate good performance of our algorithms in multiple settings, particularly in the presence of large quantities of noise [9].

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13

BAI, Z. D. "HIGH DIMENSIONAL DATA ANALYSIS." COSMOS 01, no. 01 (May 2005): 17–27. http://dx.doi.org/10.1142/s0219607705000115.

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We present two examples to show how the classical multivariate statistical approaches significantly lose efficiency or do not even work when dealing with high dimensional data analysis. These underline the importance and urgency of developing new theories to fit the urgent need of high dimensional data analysis.

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14

Bouveyron, C., S. Girard, and C. Schmid. "High-dimensional data clustering." Computational Statistics & Data Analysis 52, no. 1 (September 2007): 502–19. http://dx.doi.org/10.1016/j.csda.2007.02.009.

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15

John, Sachin Basil, and Christoph Koch. "High-Dimensional Data Cubes." Proceedings of the VLDB Endowment 15, no. 13 (September 2022): 3828–40. http://dx.doi.org/10.14778/3565838.3565839.

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This paper introduces an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. The approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly reconstructed using statistical or linear programming techniques. This enables new applications such as exploratory data analysis for feature engineering and other fields of data science. Moreover, it removes the need to compromise when building a data cube - all columns that we might ever wish to use can be included as dimensions. Our approach also speeds up certain dice, roll-up, and drill-down operations on data cubes with hierarchical dimensions compared to traditional data cubes.

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16

Chernozhukov, Victor, Denis Chetverikov, Kengo Kato, and Yuta Koike. "High-Dimensional Data Bootstrap." Annual Review of Statistics and Its Application 10, no. 1 (March 10, 2023): 427–49. http://dx.doi.org/10.1146/annurev-statistics-040120-022239.

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This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectangles, bootstrap consistency results in high dimensions, and key techniques used to establish those results. We then review selected applications of high-dimensional bootstrap: construction of simultaneous confidence sets for high-dimensional vector parameters, multiple hypothesis testing via step-down, postselection inference, intersection bounds for partially identified parameters, and inference on best policies in policy evaluation. Finally, we also comment on a couple of future research directions.

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17

Assent, Ira. "Clustering high dimensional data." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, no. 4 (June 22, 2012): 340–50. http://dx.doi.org/10.1002/widm.1062.

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18

Feng, Sheng, Liping Zhao, Haiyan Shi, Mengfei Wang, Shigen Shen, and Weixing Wang. "One-dimensional VGGNet for high-dimensional data." Applied Soft Computing 135 (March 2023): 110035. http://dx.doi.org/10.1016/j.asoc.2023.110035.

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19

Kelkar, Bhagyashri A., and Dr S. F. Rodd. "A Review of Feature Selection Techniques for Clustering High Dimensional Structured Data." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 176–79. http://dx.doi.org/10.9756/bijsesc.8270.

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20

Buja, Andreas, Dianne Cook, and Deborah F. Swayne. "Interactive High-Dimensional Data Visualization." Journal of Computational and Graphical Statistics 5, no. 1 (March 1996): 78. http://dx.doi.org/10.2307/1390754.

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21

Buja, Andreas, Dianne Cook, and Deborah F. Swayne. "Interactive High-Dimensional Data Visualization." Journal of Computational and Graphical Statistics 5, no. 1 (March 1996): 78–99. http://dx.doi.org/10.1080/10618600.1996.10474696.

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22

Zhu, Xiaofeng, Zhi Jin, and Rongrong Ji. "Learning high-dimensional multimedia data." Multimedia Systems 23, no. 3 (July 25, 2016): 281–83. http://dx.doi.org/10.1007/s00530-016-0524-7.

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23

Sapatinas, Theofanis. "Statistics for high-dimensional data." Journal of Applied Statistics 39, no. 10 (October 2012): 2308–9. http://dx.doi.org/10.1080/02664763.2012.694258.

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24

Lee, C., and D. A. Landgrebe. "Analyzing high-dimensional multispectral data." IEEE Transactions on Geoscience and Remote Sensing 31, no. 4 (July 1993): 792–800. http://dx.doi.org/10.1109/36.239901.

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25

Sharma, Anurag. "Comparing the Performance of Different High Dimensional Variable Selection Techniques on the Low Dimensional HIV/AIDS Data set." Journal of Communicable Diseases 52, no. 01 (April 30, 2020): 14–21. http://dx.doi.org/10.24321/0019.5138.202003.

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26

Desai, Keyur H., and John D. Storey. "Cross-Dimensional Inference of Dependent High-Dimensional Data." Journal of the American Statistical Association 107, no. 497 (March 2012): 135–51. http://dx.doi.org/10.1080/01621459.2011.645777.

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27

Tan, Kian-Lee, Cui Yu, St�phane Bressan, and Beng Chin Ooi. "Querying high-dimensional data in single-dimensional space." VLDB Journal The International Journal on Very Large Data Bases 13, no. 2 (May 1, 2004): 105–19. http://dx.doi.org/10.1007/s00778-004-0121-9.

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28

Inselberg, Alfred. "Visualization and data mining of high-dimensional data." Chemometrics and Intelligent Laboratory Systems 60, no. 1-2 (January 2002): 147–59. http://dx.doi.org/10.1016/s0169-7439(01)00192-7.

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29

., Shyam Mohan J. S. "DATA REDUCTION TECHNIQUES FOR HIGH DIMENSIONAL BIOLOGICAL DATA." International Journal of Research in Engineering and Technology 05, no. 02 (February 25, 2016): 319–24. http://dx.doi.org/10.15623/ijret.2016.0502058.

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30

Huang, Chao, Martin Styner, and Hongtu Zhu. "Clustering High-Dimensional Landmark-Based Two-Dimensional Shape Data." Journal of the American Statistical Association 110, no. 511 (July 3, 2015): 946–61. http://dx.doi.org/10.1080/01621459.2015.1034802.

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31

Najib, Fatma M., Rasha M. Ismail, Nagwa L. Badr, and Tarek F. Gharib. "Incomplete high dimensional data streams clustering." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 4227–43. http://dx.doi.org/10.3233/jifs-200297.

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Many recent applications such as sensor networks generate continuous and time varying data streams that are often gathered from multiple data sources with some incompleteness and high dimensionality. Clustering such incomplete high dimensional streaming data faces four constraints which are 1) data incompleteness, 2) high dimensionality of data, 3) data distribution, 4) data streams’ continuous nature. Thus, in this paper, we propose the Subspace clustering for Incomplete High dimensional Data streams (SIHD) framework that overcomes the above clustering issues. The proposed SIHD provides continuous missing values imputation for incomplete streams based on the corresponding nearest-neighbors’ intervals. An adaptive subspace clustering mechanism is proposed to deal with such incomplete high dimensional data streams. Our experimental results using two different data sets prove the efficiency of the proposed SIHD framework in clustering such incomplete high dimensional data streams in terms of accuracy, precision, sensitivity, specificity, and F-score compared to five algorithms GFCM, GBDC-P2P, DS, Ensemble, and DMSC. The proposed SIHD improved: 1) the accuracy on average over the five algorithms in the same mentioned order by 11.3%, 10.8%, 6.5%, 4.1%, and 3.6%, 2) the precision by 15%, 10.6%, 6.4%, 4%, and 3.5%, 3) the sensitivity by 16.6%, 10.6%, 5.8%, 4.2%, and 3.6%, 4) the specificity by 16.8%, 10.9%, 6.5%, 4%, and 3.5%, 5) the F-score by 16.6%, 10.7%, 6.6%, 4.1%, and 3.6%.

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32

Potts, Daniel, and Michael Schmischke. "Interpretable Approximation of High-Dimensional Data." SIAM Journal on Mathematics of Data Science 3, no. 4 (January 2021): 1301–23. http://dx.doi.org/10.1137/21m1407707.

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33

L, Anusha, and Nagaraja G. S. "Outlier Detection in High Dimensional Data." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 128–30. http://dx.doi.org/10.35940/ijeat.e2675.0610521.

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Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

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34

Novikov, Boris Asenovich, and Ivan Viсtorovich Sudos. "Data Indexing in High Dimensional Space." SPIIRAS Proceedings 2, no. 33 (April 15, 2014): 24. http://dx.doi.org/10.15622/sp.33.2.

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35

Kamalov, Firuz, and Ho Hon Leung. "Outlier Detection in High Dimensional Data." Journal of Information & Knowledge Management 19, no. 01 (March 2020): 2040013. http://dx.doi.org/10.1142/s0219649220400134.

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High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on dataset of small size with a large number of features. In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. The proposed method is designed to address the challenges of dealing with high-dimensional data by projecting the original data onto a smaller space and using the innate structure of the data to calculate anomaly scores for each data point. Numerical experiments on synthetic and real-life data show that our method performs well on high-dimensional data. In particular, the proposed method outperforms the benchmark methods as measured by [Formula: see text]-score. Our method also produces better-than-average execution times compared with the benchmark methods.

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36

Begum, Munni, Jay Bagga, and C. Ann Blakey. "Graphical Modeling for High Dimensional Data." Journal of Modern Applied Statistical Methods 11, no. 2 (November 1, 2012): 457–68. http://dx.doi.org/10.22237/jmasm/1351743360.

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37

M, BHARGAV, and VENKATESWARLU REDDY P. "SUBSPACE CLUSTERING ON HIGH DIMENSIONAL DATA." i-manager’s Journal on Cloud Computing 3, no. 3 (2016): 18. http://dx.doi.org/10.26634/jcc.3.3.8297.

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38

Adachi, Yoshitaka, and Sunao Sadamatsu. "High dimensional data driven statistical mechanics." Microscopy 63, suppl 1 (October 30, 2014): i4—i5. http://dx.doi.org/10.1093/jmicro/dfu086.

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39

Johnstone, Iain M., and D. Michael Titterington. "Statistical challenges of high-dimensional data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1906 (November 13, 2009): 4237–53. http://dx.doi.org/10.1098/rsta.2009.0159.

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Modern applications of statistical theory and methods can involve extremely large datasets, often with huge numbers of measurements on each of a comparatively small number of experimental units. New methodology and accompanying theory have emerged in response: the goal of this Theme Issue is to illustrate a number of these recent developments. This overview article introduces the difficulties that arise with high-dimensional data in the context of the very familiar linear statistical model: we give a taste of what can nevertheless be achieved when the parameter vector of interest is sparse, that is, contains many zero elements. We describe other ways of identifying low-dimensional subspaces of the data space that contain all useful information. The topic of classification is then reviewed along with the problem of identifying, from within a very large set, the variables that help to classify observations. Brief mention is made of the visualization of high-dimensional data and ways to handle computational problems in Bayesian analysis are described. At appropriate points, reference is made to the other papers in the issue.

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40

Kouropteva, O., O. Okun, and M. Pietikäinen. "Semisupervised visualization of high-dimensional data." Pattern Recognition and Image Analysis 17, no. 4 (December 2007): 612–20. http://dx.doi.org/10.1134/s1054661807040220.

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41

Shneiderman, B. "A telescope for high-dimensional data." Computing in Science & Engineering 8, no. 2 (March 2006): 48–53. http://dx.doi.org/10.1109/mcse.2006.21.

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42

Kim, HyunJi, Byong Su Choi, and Moon Yul Huh. "Booster in High Dimensional Data Classification." IEEE Transactions on Knowledge and Data Engineering 28, no. 1 (January 1, 2016): 29–40. http://dx.doi.org/10.1109/tkde.2015.2458867.

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43

Genender-Feltheimer, Amy. "Visualizing High Dimensional and Big Data." Procedia Computer Science 140 (2018): 112–21. http://dx.doi.org/10.1016/j.procs.2018.10.308.

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44

Aggarwal, Charu C., and Philip S. Yu. "Outlier detection for high dimensional data." ACM SIGMOD Record 30, no. 2 (June 2001): 37–46. http://dx.doi.org/10.1145/376284.375668.

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45

Ro, Kwangil, Changliang Zou, Zhaojun Wang, and Guosheng Yin. "Outlier detection for high-dimensional data." Biometrika 102, no. 3 (June 7, 2015): 589–99. http://dx.doi.org/10.1093/biomet/asv021.

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46

Parsons, Lance, Ehtesham Haque, and Huan Liu. "Subspace clustering for high dimensional data." ACM SIGKDD Explorations Newsletter 6, no. 1 (June 2004): 90–105. http://dx.doi.org/10.1145/1007730.1007731.

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47

Kuelbs, Jim, and Anand N. Vidyashankar. "Asymptotic inference for high-dimensional data." Annals of Statistics 38, no. 2 (April 2010): 836–69. http://dx.doi.org/10.1214/09-aos718.

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48

Tian, Cuihua, Yan Wang, Xueqin Lin, Jing Lin, and Jiangshui Hong. "Research on High-Dimensional Data Reduction." International Journal of Database Theory and Application 9, no. 1 (January 31, 2016): 87–96. http://dx.doi.org/10.14257/ijdta.2016.9.1.08.

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49

Ullah, Insha, and Beatrix Jones. "Regularised Manova for High-Dimensional Data." Australian & New Zealand Journal of Statistics 57, no. 3 (August 6, 2015): 377–89. http://dx.doi.org/10.1111/anzs.12126.

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50

Kim, Kyung In, and Richard Simon. "Probabilistic classifiers with high-dimensional data." Biostatistics 12, no. 3 (November 17, 2010): 399–412. http://dx.doi.org/10.1093/biostatistics/kxq069.

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