Nature Methods - A shortcut to high-dimensional data. Big data comes at a price, and it is a glutton for computer memory. Rather than using data compression to solve this problem, Cleary et al ...

Figure 2. High-speed compression reduces interconnect bottlenecks between data converters and FPGAs, and for sampled data transfers across busses, networks, and storage systems. In applications such ...

Getting a high compression ratio is needed to address the increasing gap between the memory size and the storage system bandwidth and size in exascale systems. The EZ project is developing the SZ ...

Statisticians from the National University of Singapore (NUS) have introduced a new technique that accurately describes high-dimensional data using lower-dimensional smooth structures. This ...

Alireza Doostan is leading a major effort for real-time data compression for supercomputer research.. A professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences at the ...

In high dimensional data, clusters of objects often exist in subspaces rather than in the entire space. This is the data sparsity problem faced in clustering high-dimensional data.

A final drawback of LZ-based algorithms for high-speed sampled data is their lack of support for lossy compression. With Samplify, lossy compression is integrated into the algorithm. To summarize, ...

Jan 29, 2024: A manifold fitting approach for high-dimensional data reduction beyond Euclidean space (Nanowerk News) National University of Singapore (NUS) statisticians have introduced a new ...