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A telescope for high-dimensional data
Computing in Science & Engineering, 2006Muscular dystrophy is a degenerative disease that destroys muscles and ultimately kills its victims. Researchers worldwide are racing to find a cure by trying to uncover the genetic processes that cause it. Given that a key process is muscle development, researchers at a consortium of 10 institutions are studying 1,000 men and women, ages 18 to 40 ...
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Clustering high dimensional data
WIREs Data Mining and Knowledge Discovery, 2012AbstractHigh‐dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so‐called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known to render traditional clustering algorithms ...
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Dynamics of ICA for High- Dimensional Data
2002The learning dynamics close to the initial conditions of an on-line Hebbian ICA algorithm has been studied. For large input dimension the dynamics can be described by a diffusion equation.A surprisingly large number of examples and unusually low initial learning rate are required to avoid a stochastic trapping state near the initial conditions.
Basalyga, Gleb, Rattray, Magnus
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A partitioning method for high dimensional data
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication, 2010Nearest neighbor search in high-dimensional space is an important operation in many applications, such as data mining and multimedia database. Evaluating similarities of a point to all other points in high-dimensional space need the high computational cost. For reducing the computational cost, index-structures are frequently used.
Seunghoon Lee +4 more
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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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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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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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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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Clustering of High Dimensional Data Streams
2012Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex for clustering. As such there is a growing need for algorithms that can be applied on streaming data and the at same time can cope ...
Sotiris K. Tasoulis +2 more
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