CUDA-Accelerated HD-ODETLAP: Lossy High Dimensional Gridded Data Compression

2013 ◽  
pp. 95-111
Author(s):  
W. Randolph Franklin ◽  
You Li ◽  
Tsz-Yam Lau ◽  
Peter Fox
Keyword(s):  
Data Compression ◽  
High Dimensional ◽  
Gridded Data

         Towards Analysing multivariate weather/climate extremes

2021 ◽  
Author(s):  
Abdel Hannachi ◽  
Nickolay Trendafilov
Keyword(s):  
Time Series ◽  
Convex Hull ◽  
Optimization Algorithm ◽  
Climate Extremes ◽  
High Dimensional ◽  
Gridded Data ◽  
Future Perspectives ◽  
Archetypal Analysis ◽  
Univariate Time Series ◽  
Dimensional State Space

<p>Extreme analysis, via e.g., GEV, was developed to deal with univariate time series, and is very difficult to extend beyond that dimension. Here we explore a different method, the archetypal analysis, which focuses on multivariate extremes. The method seeks to approximate the convex hull in high-dimensional state space, by identifying corners representing "pure" types, i.e. archetypes. The method, encompasses, in particular, the virtues of EOFs and clustering. The method is presented with a new manifold-based optimization algorithm, and applied to a number of atmospheric fields, including SST and SLP gridded data. The application to SST, in particular, reveals important features related to SST extremes. The strengths and weaknesses of the method and possible future perspectives will be discussed.</p>

scholarly journals High-dimensional data compression via PHLCT

2007 ◽  
Author(s):  
Zhihua Zhang ◽  
Naoki Saito
Keyword(s):  
Data Compression ◽  
High Dimensional Data ◽  
High Dimensional

High-Dimensional Probability

2018 ◽  
Author(s):  
Roman Vershynin
Keyword(s):  
High Dimensional

The Semantic Credentials of High-Dimensional Memory Models

2014 ◽  
Author(s):  
Curt Burgess ◽  
Sarah Maples
Keyword(s):  
Memory Models ◽  
High Dimensional
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