Results 261 to 270 of about 1,253,621 (308)

High-Dimensional Data Cubes

Proceedings of the VLDB Endowment, 2022
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.
Sachin Basil John, Christoph Koch 0001
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Forecasting high-dimensional data

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010
We propose a method for forecasting high-dimensional data (hundreds of attributes, trillions of attribute combinations) for a duration of several months. Our motivating application is guaranteed display advertising, a multi-billion dollar industry, whereby advertisers can buy targeted (high-dimensional) user visits from publishers many months or even ...
Deepak Agarwal, Datong Chen, Long-ji Lin, Jayavel Shanmugasundaram, Erik Vee   +4 more
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Feature selection for high-dimensional data

Computational Management Science, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DESTRERO A, MOSCI, SOFIA, DE MOL C, VERRI, ALESSANDRO, ODONE, FRANCESCA   +4 more
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High-dimensional Data Cubes

ACM Transactions on Database Systems
We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly approximated using statistical or linear programming techniques.
Sachin Basil John, Christoph Koch 0001, Peter Lindner 0001   +2 more
openaire   +1 more source

Clustering high dimensional data

WIREs Data Mining and Knowledge Discovery, 2012
AbstractHigh‐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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A telescope for high-dimensional data

Computing in Science & Engineering, 2006
Muscular 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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Dimensionality Reduction for Registration of High-Dimensional Data Sets

IEEE Transactions on Image Processing, 2013
Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the
Xu, Min, Chen, Hao, Varshney, Pramod K.
openaire   +3 more sources

Dimensionality reduction for high dimensional data

Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, 2014
Information Technology has produced huge amounts of data and these data need to be processed to extract information hidden in it. Feature selection techniques often come handy to process these data efficiently. In this paper, a novel approach for feature selection GA-CFS is proposed.
Aditya Kumar, Smita Roy, Prabhat Ranjan
openaire   +1 more source

Dynamics of ICA for High- Dimensional Data

2002
The 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
openaire   +2 more sources

On High Dimensional Indexing of Uncertain Data

2008 IEEE 24th International Conference on Data Engineering, 2008
In 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
openaire   +1 more source