Non-linear dimensionality reduction methods for large, high dimensional datasets.

Speaker: Prof. Boudewijn Lelieveldt (TU Delft)

About the seminar

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, which scales to millions of data points while preserving the manifold structure of the full dataset. Applications of these techniques will be highlighted in three application domains: 1) analysis of hyperspectral imaging data 2) linked analysis of spatio-temporal gene expression data, and 3) HDPS: a generic plugin system for fast and interactive analysis of high-dimensional data.