Synthetic Data and Generative AI

Chapter 5: Synthetic clusters and alternative to GMM

Fast classification and clustering via image convolution filters

Abstract

Here I generate synthetic data using a superimposition of stochastic processes, comparing it to Bayesian generative mixture models (Gaussian mixtures or GMM). I explain the benefits and differences. The actual classification and clustering algorithms are model-free, and performed in GPU as image filters, after transforming the raw data into an image. I then discuss the generalization to 3D or 4D, and to higher dimensions with sparse tensors. The technique is particularly suitable when the number of observations is large, and the overlap between clusters is substantial.

It can be done using few iterations and a large filter ...