high dimensional datachemometricsinteractionsMonte Carlo simulationslow dimensional smoothers/ C7320 Physics and chemistry computing C1140 Probability and statisticsOne important issue in chemometrics is to detect interactions among several factors. The authors propose methods that detect interactions using low ...
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data ...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n≪p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data s...
With the advent of massive data sets, much of the computational science and engineering community has moved toward data-intensive approaches in regression
FIGURE 5. Three-dimensional perspective impedance plot of β-PbF2 data (——–-) and fitted values and curves (———); the fitting circuit used and parameter estimates and estimates of their standard deviations. (Reprinted by permission of John Wiley & Sons, Inc., from “Impedance Spec...
Efficiency: By moving from the high-dimensional image space to a lower-dimensional latent space, DMs become computationally efficient. Inductive Bias: The UNet architecture of DMs is particularly effective for data with spatial structure, reducing the need for aggressive compression. Versatility: The la...
X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take
The mathematical and statistical properties of high-dimensional data spaces are often poorly understood or inadequately considered. This can be particularly challenging for the common scenario where the number of data points obtained for each specimen greatly exceed the number of specimens. ...
This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and ...