… The statistical reality unfortunately clashes with this optimistic statement: Separating the
signal from the noise is in general almost impossible in high-dimensional data. This …

… high-dimensional spaces can behave in a drastically different way from our intuition (mostly
gained from … not to remove the difficulties related to high-dimensional data, but to circumvent …

… our ability to extract useful information from high dimensional data. There are several … ,
statisticians can now easily interact with the high dimensional data by 3-D rotation plots, …

… from considerable computational costs, preventing the state-ofthe-art methods such as the
t-SNE from scaling to largescale and high-dimensional … -nearest neighbor graph from the data …

… that arise with high-dimensional data in the … , from within a very large set, the variables
that help to classify observations. Brief mention is made of the visualization of high-dimensional …

… statistical and biological information from high-dimensional data spaces, … From the
perspective of translational science, this Review discusses the properties of high-dimensional …

… and their amazing properties largely result from the sophisticated hierarchical structures.
Following … with the emphasis on mimicking the structure from one to three dimensions. Selected …

… Here, we demonstrate that complex, high-dimensional classifiers can also be trained on …
avoid relying exclusively on simulations for high-dimensional classification. This work opens the …

… to each other than cells from other clusters. Because these … correlations in high-dimensional
data (m ≥ 3) from a series … to extract a hierarchy from high-dimensional cytometry data in …

… of physical models and mathematical equations directly from data is becoming increasingly
possible… high-dimensional differential equations (which we will refer to as the ``model"") from …