Design at Large - Laurens van der Maaten, Visualizing Data Using Embeddings

February 10, 2016
Fung Auditorium, UC San Diego

This talk by Facebook artificial intelligence researcher Laurens van der Maaten is part of the Winter 2016 Design@Large series organized by The Design Lab.

Visualization techniques are essential tools for every data scientist. Unfortunately, the majority of visualization techniques can only be used to inspect a limited number of variables of interest simultaneously. As a result, these techniques are not suitable for big data that is very high-dimensional.

An effective way to visualize high-dimensional data is to represent each data object by a two-dimensional point in such a way that similar objects are represented by nearby points, and that dissimilar objects are represented by distant points. The resulting two-dimensional points can be visualized in a scatter plot. This leads to a map of the data that reveals the underlying structure of the objects, such as the presence of clusters.

The talk presents techniques to embed high-dimensional objects in a two-dimensional map. In particular it focuses on a technique called t-Distributed Stochastic Neighbor Embedding (t-SNE) that produces substantially better results than alternative techniques. We demonstrate the value of t-SNE in domains such as computer vision and bioinformatics. In addition, we show how to scale up t-SNE to sets with millions of objects, and we present variants of the technique that can visualize objects of which the similarities cannot appropriately be modeled in a single map (such as semantic similarities between words) and that can visualize data based on partial similarity rankings of the form "A is more similar to B than to C".