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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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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, 2010We 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 +4 more
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Feature selection for high-dimensional data
Computational Management Science, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DESTRERO A +4 more
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Clustering High-Dimensional Data
2015This chapter introduces the task of clustering, concerning the definition of a structure aggregating the data, and the challenges related to its application to the unsupervised analysis of high-dimensional data. In the recent literature, many approaches have been proposed for facing this problem, as the development of efficient clustering methods for ...
MASULLI, FRANCESCO, ROVETTA, STEFANO
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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 +2 more
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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 +2 more
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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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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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Dynamic visualization of high-dimensional data
Nature Computational Science, 2022Abstract Dimensionality reduction (DR) is commonly used to project highdimensional data into lower dimensions for visualization, which could then generate new insights and hypotheses. However, DR algorithms necessarily introduce distortions in the visualization and cannot faithfully represent all relations in the data. As such, there is
Eric D. Sun, Rong Ma, James Zou 0001
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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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