A lot of my ideas about Machine Learning come from Quantum Mechanical Perturbation Theory. To provide some context, we need to step back and understand that the familiar techniques of Machine Learning, like Spectral Clustering, are, in fact, nearly identical to Quantum Mechanical Spectroscopy. As usual, this will take several blogs.
Here, I give a brief tutorial on the theory of Spectral Clustering and how it is implemented in open source packaages
At some point I will rewrite some of this and add a review of this recent paper Robust and Scalable Graph-Based Semisupervised Learning
Spectral (or Subspace) Clustering
The goal of spectral clustering is to cluster data that is connected but not lnecessarily compact or clustered within convex boundaries
The basic idea:
- project your data into $latex R^{n} $
- define an Affinity matrix $latex A $ , using a Gaussian Kernel $latex K $ or say just an Adjacency matrix…
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