Clustering and Finding the Number of Clusters by Unsupervised Learning of Mixture Models using Vector Quantization

A new Lagrangian formulation with entropy and codebook size was proposed to extend the Lagrangian formulation of variable-rate vector quantization. We use the new Lagrangian formulation to perform clustering and to find the number of clusters by fitting mixture models to data using vector quantization. Experimental results show that the entropy and memory constrained vector quantization outperforms the state-of-the-art model selection algorithms in the examples considered.