SAILS Lunch Time Seminar
- Monday 12 September 2022
- online only
Non-linear dimensionality reduction methods for interaction exploration of high dimensional (image) data.
This presentation discusses novel visual analytics and non-linear dimensionality reduction techniques for large, high dimensional datasets. Focusing on the non-linear embedding technique tSNE, we developed Dual tSNE and linked-view tSNE to enable fast and interactive identification of clusters and functionally interesting feature sets. Moreover, we developed spatially mapped tSNE that integrates spatial image information in the tSNE map analysis. Finally, we developed Hierarchical Stochastic Neighbor Embedding, that scales to millions of data points while preserving the manifold structure of the full dataset. Applications of these techniques will be highlighted in two application examples: 1) HDPS: a generic plugin system for fast and interactive analysis of large high-dimensional data sets, 2) Data viewers developed for interactive exploration of the single-cell data resources of the Allen Institute for Brain Science in Seattle.
The SAILS Lunch Time Seminar is an online event, but it is not publicly accessible in real-time. If you would like to join this seminar, please send an email to email@example.com to receive a link.