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HIGH DIMENSIONAL DATA ANALYSIS
COSMOS, 2005We 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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2006
With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the ...
Wei Wang, Jiong Yang
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With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the ...
Wei Wang, Jiong Yang
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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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High dimensional data driven statistical mechanics
Microscopy, 2014In "3D4D materials science", there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modellingA three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to ...
Yoshitaka, Adachi, Sunao, Sadamatsu
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Clustering High-Dimensional Data
2018This chapter provides an overview on the fundamental problems that clustering is confronted with in high-dimensional data. The motivation of specialized solutions for analyzing high-dimensional data has often been given with a general reference to the so-called curse of dimensionality. With respect to spatial queries, the observation that the intrinsic
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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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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
openaire +1 more source