Gaussian Mixture Model Parameter Initialization

Before constructing a Gaussian mixture model, it is essential to initialize its parameters. One commonly used initialization method employs the KMEANS algorithm to estimate Gaussian mixture model parameters. KMEANS is among the most prevalent clustering analysis algorithms that partitions data into clusters and determines cluster centroids. In the Gaussian mixture model parameter initialization procedure, KMEANS algorithm can estimate cluster centers for the mixture model and subsequently initialize the model parameters based on these centers. The implementation typically involves calculating cluster centroids as initial mean vectors, using cluster variances to initialize covariance matrices, and deriving mixing coefficients from cluster member proportions. Therefore, parameter initialization serves as a crucial preliminary step before Gaussian mixture model training, significantly impacting convergence speed and final model quality through proper starting values for expectation-maximization algorithms.