Week in Vis 7 Mon, Oct 14-Fri, Oct 18
- Mon, Oct 14 – Mid Dimensional/High Dimensional
- Wed, Oct 16 – ICE:DC2 Workshop / High Dimensional Data
- Fri, Oct 18 – No Class
- Reading: Week 7 – High-Dimensional Data (due Mon, Oct 14 – preferably before class)
- Online Discussion 07: High-Dimensional Data (first post due Tue, Oct 15)
- Design Challenge: DC2: Task List (due Wed, Oct 16)
- Quiz 07: High-Dimensional Data (due Fri, Oct 18)
- Seek and Find 07: High-Dimensional Data (due Fri, Oct 18)
This week we’ll consider a different kind of scalability challenge: having too many dimensions. This is a hot topic in machine learning, bioinformatics, … Unfortunately, we won’t get to dig too deeply into the mathematics. But, if you’ve seen dimensionality reduction or embedding in an ML or stats class, this might give you some more basic insights.
We also move on to design Challenge 2, with a first phase due on Wednesday. The Wednesday deadline is pretty tight – we want to take your list of tasks and compile them so that everyone can see the whole list.
In class on Wednesday, I’ll take some time to talk about DC2 – but mainly to answer questions. So make sure you’ve thought about DC2 so you have questions to ask.
Readings for the Week
Last week, we focused on scaling in the number of items. This week, we’ll talk about what to do when we have too many dimensions.
- High-Dimensional Visualizations. Georges Grinstein, Marjan Trutschl, Urska Cvek. (semantic scholar) (link1)
This is an old (Circa 2001) paper that I am not sure was actually published at KDD. However, it is a great gallery of old methods for doing “High-Dimensional” (mid-dimensional by modern standards) visualizations. Most of these ideas did not stand the test of time – but it’s amusing to look through the old gallery to get a sense of what people were trying.
- The Beginner’s Guide to Dimensionality Reduction, by By: Matthew Conlen and Fred Hohman. An Idyll interactive workbook.
This is a very basic demonstration of the basic concepts of dimensionality reduction. It doesn’t say much about the “real” algorithms, but you should get a rough idea if you haven’t already.
- How to Use T-SNE Effectively
I wanted to give you a good foundation on dimensionality reduction. This isn’t it. But… it will make you appreciate why you need to be careful with dimensionality reduction (especially fancy kinds of it).