GMM – Request for Explanation

Certainly, let’s delve a bit deeper!

One key challenge in using Gaussian Mixture Models (GMMs) is determining the number of Gaussian components to use in the mixture. This is often addressed through techniques like the Bayesian Information Criterion (BIC) or cross-validation, which help select an appropriate number of components to avoid overfitting or underfitting the data.

GMMs can also be extended to more complex scenarios, such as when dealing with data that doesn’t fit the traditional Gaussian distribution. In such cases, different types of mixture models like t-distribution-based mixtures or diagonal/covariance matrix-tied mixtures can be employed to accommodate various data characteristics.

It’s worth noting that while GMMs are powerful, they may struggle with high-dimensional data or when the number of components is too large, due to the curse of dimensionality. In these cases, other techniques like dimensionality reduction or different clustering algorithms might be more suitable.