Sensory Substitution & Enhancement for Perceiving High Dimensional Data

As humans, we are severely limited in our capability to understand, visualize or perceive dimensions beyond the spatial 3 dimensions. We can also perceive changes in time, which gives us the ability to visualize 4-D changes and patterns. However, it is extremely unintuitive for us to understand spaces that are any higher than 3D. I remember reading about this first in Michio Kaku’s Hyperspace, where he constructs an analogy between us and fictional 2D beings, that can only perceive two dimensions. These beings would look at everything in 2D, so we (3D beings) would look as contours constantly changing shapes, since they are projected onto the 2D plane that those beings can see.

Today, since we have very different kinds of data that is often high dimensional – “HD” –  (different from Big Data!) there is an increasing appetite for methods that allow us to visualize patterns and changes in these HD spaces. Areas such as topological data analysis (TDA) attempts to solve these problems using tools from Topology, a field in mathematics that has been very popular since the 1930s. Ayasdi a data startup founded by Gunner Carlsson of Stanford, attempts to provide solutions to data problems using topological tools, it recently raised $55M in funding, and is seeing ~400% growth!

High dimensional data visualization is important and needs more attention from mathematicians and engineers. Interestingly a recent TED talk this year spoke about Sensory Substitution, which is basically the process where you take high dimensional real world signals such as images, audio etc. and map them to a much lower dimensional space and feed the low dimensional signal to the brain via different sensory inputs such as electrodes on the tongue, or tactic feedback on the skin etc. The claim is that the brain can automatically learn to “see” or “hear” given a few weeks of training with these new “eyes” or “ears”, since all perception happens in the brain. It is very inspiring and gives a lot of hope for people who have one or more of their primary senses which do not work as they were designed.

Now imagine, instead of “visualizing” 3D data all the time what if we could map dimension #4 to an auditory signal, #5 to a tactile signal, #6 to some other type of signal and have a human “perceive” the data (here I refer to perception as the generalization of visualization). How effective are we, as humans, in obtaining all this information at once and understanding the patterns in the high dimensional space? We will encounter cognitive overload, where beyond a certain point our brain cannot make any sense of the patterns. But as long as we operate within that limit, it will be very exciting to see how we understand 6- dimensional data. An added benefit is that we can map very HD data to 6 dimensions which a whole lot better than mapping it to 3 dimensions (see curse of dimensionality).

Going even further, if we perfect the art of data perception in HD spaces, can we also perceive why some machine learning algorithms fail and some don’t? Can we perceive boundaries between classes in massive datasets? The interesting thing about these questions is that I am not sure who could answer it better – a neuroscientist or an engineer!

A Reading Group for Computer Vision, Machine Learning & Pattern Recognition at Arizona State University

[edit: Jan 20, 2016] This post was written a long ago, when I was interested in figuring out who was working on what within ASU. It turned out to be a lot harder than I expected to get something like this started, and I have since graduated from ASU. However, I hope this post will serve as an (incomplete) entry point into some of the vision research that’s being conducted at ASU. There have also been several newer faculty who have joined with exciting research areas!

With every passing day, I realize more – how important it is for me to document my opinions, findings and thoughts on several topics that I read. Not only does this help me learn faster, expose myself to a broader spectrum of papers and ideas but also helps me create this portfolio of my works. I realize this is something that’s important to every PhD aspirant. It is my understanding that doing a PhD is a lot like a beginner’s course in Entrepreneurship. You have to develop ideas, form opinions and sell them to your community. Of course, you don’t go out of business and have your start-up crashing down if your product doesn’t sell, but you have different pressures like establishing your ideas firmly.

Reading Group
With those side notes apart, I wish to begin a small reading group here at ASU. I have read about a similar group in CMU (http://www.cs.cmu.edu/~misc-read/) that is well established now. Inspired by this blog post, a similar group will do folks at ASU a lot of good.

Research Groups working on Computer Vision and/or Machine Learning at ASU:
Although ASU’s research in EE is strongly towards communication, networking etc. Few professors are changing the research landscape to add to computer vision research. The CS department, however, is known to have strong faculty working on Machine Learning, Pattern Recognition and some computer vision. I shall try to note all the groups here in order to make a comprehensive list of people who will be the ideal audience for the aforementioned seminar sessions.

Electrical, Computer & Energy Engineering:

    • Dr. Andreas Spanias’ group (Main contributors [1], [2]) –  Sparse representations, dictionary learning methods, high dimensional geometry etc.
    • Dr. Lina Karam’s group – Well established group, work mainly on Compression codecs, Visual quality assessment, Saliency in Images/Videos etc.
    • Dr. Pavan Turaga‘s group – Relatively new, main focus – topics on activity analysis, compressive sensing, dictionary learning, non linear geometries etc.

Next is the CS group, with many more faculty working on different aspects of these areas. I am less familiar with them and hence will just list them (in no particular order) for record’s sake.

Computing, Informatics and Decision Systems Engineering:

So assuming each of them have around 5 students and out of that at least 2 are interested in this reading session that gives us around ~15 students to start off this with. Which seems like a reasonable number. Hopefully, if the graduate student association of ASU (GPSA) recognizes this as a grad organization, they’ll even fund some part of it.

More updates as things progress.

— Rushil