3 ms·
Makes sense. A graph can be represented by a matrix which is what an image is.
by sjg007 6y ago
Makes sense. A graph can be represented by a matrix which is what an image is.
- oivey 6y agoImages and matrices are 2D data structures of numbers, but that is where the similarities end. An image is more like a vector, which matrices can be applied to. You would never matrix multiply an image onto another vector. Still, it isn’t uncommon to visualize matrices as images.
- sjg007 6y agoWell a matrix is a collection of vectors so... I guess I somewhat agree.. You can certainly apply projections to images, I mean this is what photoshop does.
- gspr 6y ago> Well a matrix is a collection of vectors so That's like saying "a matrix is a collection of real numbers, so anything you say about one applies to the other". > You can certainly apply projections to images, I mean this is what photoshop does. This doesn't seem to refer to anything in the comment you're replying to.
- sjg007 6y agoWould you please elaborate on your last point?
- gspr 6y agoIn reply to a comment that said nothing about projections, you wrote: > You can certainly apply projections to images, I mean this is what photoshop does. What's the relationship of this to anything in the comment you replied to?
- sjg007 6y ago"You would never matrix multiply an image onto another vector."
- gspr 6y ago> "You would never matrix multiply an image onto another vector." That wasn't me. But I can still elaborate: while you can certainly consider a non-color image as a matrix, the operation of multiplying this matrix with a vector is rather meaningless. While a lot of things can be made into or viewed as matrices, a matrix is typically only meaningful as a representation of a linear map.
- ladberg 6y agoEven if you never matrix multiply with an image, it's still useful to have it in matrix form for other things like PCA/SVD.
- newen 6y agoTypical use on PCA/SVD on image, what you do is treat each image as a vector, create a matrix out of a collection of images, and then do PCA/SVD on the matrix to analyze the distribution of the images, normalize, get the eigen-images (principal components), etc.
- ladberg 6y agoYeah, in retrospect that seems like the way to do it. The toy examples I learned from in college did it on a single image split up by row, but I can’t think of a great use case for that besides some naive compression.
- gspr 6y agoAn image "is" not a matrix. Yes, the values of each pixel can be considered an entry of a matrix, but that's where the similarity ends. Unless the graph is very dense, doing convolutions on the image representing the graph is a pretty arbitrary thing to do. Graph CNNs exist and make a lot more sense in general.