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[D] CNN on irregular grid

Here’s what my data look like:

  • There is a collection of a few thousand objects
  • Each object is sampled on a grid, with multiple properties observed per point
  • There are two types of objects

And the goal is to identify which of the two types of objects we have given the grid of samples.

So this looks like a straightforward CNN project. I’m not an expert, but I have experience fitting those on RGB images using TF/keras and doing some model tuning and validation. Should be easy, right?

The problem is that these grids are all irregular. I know how to fit CNNs when I have a collection of rectangular images all of the same size (if they’re different dimensions, then it’s straightforward to resize/interpolate/resample them). But how do you handle grids taken from what I can best describe as “blobs”? To make things even harder, the sampling instrument occasionally malfunctioned, so I have occasional NAs for the measurements. As far as my domain knowledge goes, the shape of the grid or the frequency of the instrument failing to make a valid measurement shouldn’t depend on the classification (or vice versa).

My thinking is perhaps there’s a way to reshape everything into rectangles of the same size, but I can’t think of how. Or is there another totally different type of model that would work best for these data?

submitted by /u/koobear
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.