Hierarchical Stochastic Neighbor Embedding
By Nicola Pezzotti, Thomas Höllt, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova. 2016
152 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Boudewijn P. F. Lelieveldt
- Anna Vilanova
- Elmar Eisemann
- Nicola Pezzotti
- Thomas Höllt
- J. Snyder-Cappione
- Marcel J. T. Reinders
- Josef Spidlen
In Computer Vision and Pattern Recognition
- Boudewijn P. F. Lelieveldt
- Anna Vilanova
- Nicola Pezzotti
- Thomas Höllt
- Elmar Eisemann
- Alexander Mordvintsev
- J. Snyder-Cappione
- Marcel J. T. Reinders
Citations in the corpus
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “Several attempts to successfully apply t-SNE-like methods to massive datasets have been recently reported including afore-noted HSNE 7,17,35 , LargeVis 8 , and net-SNE 36 .”
- Linear tSNE optimization for the Web (2018, methodology): “A completely different approach is taken in the Hierarchical Stochastic Neighbor Embedding algorithm (HSNE) [19].”
- Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types (2017, methodology): “HSNE builds a hierarchy of local and non-linear similarities of high-dimensional data points 13 , where landmarks on a coarser level of the hierarchy represent a set of similar points or landmarks of the preceding more detailed level.”