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On High Dimensional Indexing of Uncertain Data
2008 IEEE 24th International Conference on Data Engineering, 2008In this paper, we will examine the problem of distance function computation and indexing uncertain data in high dimensionality for nearest neighbor and range queries. Because of the inherent noise in uncertain data, traditional distance function measures such as the Lq-metric and their probabilistic variants are not qualitatively effective.
Charu C. Aggarwal, Philip S. Yu
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Learning high-dimensional multimedia data
Multimedia Systems, 2016The internet revolution has made information acquisition easy and cheap and has been producing massive high-dimensional multimedia data, including text, audio, images, animation, video, etc. High-dimensional multimedia data bring new opportunities to modern society and challenges to researchers of the multimedia domain as well. The goal of this special
Xiaofeng Zhu 0001 +2 more
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Melvin Geubbelmans +3 more
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Melvin Geubbelmans +3 more
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High-dimensional Data Visualization
2007One of the biggest challenges in data visualization is to find general representations of data that can display the multivariate structure of more than two variables. Several graphic types like mosaicplots, parallel coordinate plots, trellis displays, and the grand tour have been developed over the course of the last three decades.
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2019
We have a dataset that is a collection of d-dimensional vectors. This chapter introduces the nasty tricks that such data can play. A dataset like this is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean).
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We have a dataset that is a collection of d-dimensional vectors. This chapter introduces the nasty tricks that such data can play. A dataset like this is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean).
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Regularised Manova for High‐Dimensional Data
Australian & New Zealand Journal of Statistics, 2015SummaryThe traditional and readily available multivariate analysis of variance (MANOVA) tests such as Wilks' Lambda and the Pillai–Bartlett trace start to suffer from low power as the number of variables approaches the sample size. Moreover, when the number of variables exceeds the number of available observations, these statistics are not available ...
Ullah, Insha, Jones, Beatrix
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Visual analytics for high dimensional data
Proceedings of the 2018 International Conference on Advanced Visual Interfaces, 2018A dataset with M items has 2M subsets anyone of which may be the one satisfying our objective. With a good data display and interactivity our fantastic pattern-recognition defeats this combinatorial explosion by extracting insights from the visual patterns. This is the core reason for data visualization.
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High-Dimensionality Data Reduction with Java
Computing in Science & Engineering, 2008The author used the Java multimedia framework along with statistical software from the Colt project to preprocess and compare videos downloaded from Internet sites. Preprocessing the videos decreases each one's dimensionality and size, making it easier to analyze, interpret, and convey useful information about the data's most relevant attributes.
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High Dimensional Visual Data Classification
2007We present new visual data mining algorithms for interactive decision tree construction with large datasets. The size of data stored in the world is constantly increasing but the limits of current visual data mining (and visualization) methods concerning the number of items and dimensions of the dataset treated are well known (even with pixellisation ...
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Understanding High Dimensional Visual Data
2019Researchers are increasingly using digital images to teach machines how to see and understand our physical world. While conventional wisdom states that a picture is worth a thousand words and seeing is believing, it is now important to ask what a picture is really worth and how much trust we can place in vision alone.
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