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Self Organizing Maps

6/18/2018

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Recently I've become really fascinated by the idea of manifold learning, the main idea behind it being that most data sets are actually too high-dimensional, and we can reduce the dimension of the data by viewing it as a manifold.

Since I need to start preparing for a senior thesis, I decided that I want to try and implement some manifold learning techniques in order to better understand how all of it currently works. The simplest (at least at first glance to me) technique was called a "Self Organizing Map".

Most C++ implementations of SOMs are restricted to two dimensions, and I wanted to understand SOMs for myself; so I decided to make my own. The current iteration of it is here.
The image to the right is an example of an output of an SOM, it is a 2 dimensional clustering of the colors based off of eight random colors. You'll notice how similar colors sorta ended up in similar places (with the exception of that one lost colony of green), and how there are usually nice gradients between everything.
Picture
But what is a SOM?

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Lesson 5 out Now

6/13/2018

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Lesson 5 is out here!

And that about concludes the ROS/Gazeebo tutorial I've been working on, I'm gonna go back and improve it a bit, but that's the basic outline.
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    Hi, I'm Billy, a PhD student studying Math.

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  • Blog
  • Projects and Papers
  • Lecture Notes
  • ROS/Gazebo Tutorial
    • Lesson 1: Set-up
    • Lesson 2: URDF and XACRO
    • Lesson 3: Robot Plugins
    • Lesson 4: Programming our Rover
    • Lesson 5: Adding a LIDAR
  • Contact