Previously we’ve seen how Procrustes analysis works: https://subokita.com/2014/04/07/procrustes-analysis/. Here’s a generalised version, that attempts to find a mean shape out from a set of points / shapes.
Procrustes Analysis is a method to compare shapes of two or more objects, by first performing superimposition, i.e. rotating, translating, and uniformly scaling the objects so that they fit each other best. Here’s an example of it, and sample C++ OpenCV code for it.
Here’s a sample of Spectral Clustering code on C++ and OpenCV. It’s basically very similar to the python version that I posted earlier: https://subokita.com/2014/03/04/k-means-and-spectral-clusterings/
ICA / Independent Component Analysis, is one of the decomposition methods that is capable of decomposing multivariate signals into components, based on the assumption that the source components are statistically independent from each other, and follow a non-gaussian distribution.
Recently I read a post on reddit/r/machinelearning that talks about intuition behind spectral clustering. I was interested, because all I have used are K-means and Meanshift clusterings before. Thus after writing out test codes in python, I decided to post about it.
Sorry for the shilling, but here’s my upcoming project:
Please register your email address if you’re interested in it.
Edit: Sadly by the request of Coursera itself, I’ve removed this particular github repository. Coursera is doing the right thing though, don’t blame them.
I applied and followed Machine Learning course which is taught by Andrew Ng and offered freely online on Coursera (https://www.coursera.org/course/ml). However since the Octave installed on my computer doesn’t work properly (e.g. plotting functionality doesn’t work at all, etc), I decided to use python instead.