Gaussian mixture models are a probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don’t require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning.
For example, in modeling human height data, height is typically modeled as a normal distribution for each gender with a mean of approximately 5’10” for males and 5’5” for females. Given only the height data and not the gender assignments for each data point, the distribution of all heights would follow the sum of two scaled (different variance) and shifted (different mean) normal distributions. A model making this assumption is an example of a Gaussian mixture model (GMM), though in general, a GMM may have more than two components. Estimating the parameters of the individual normal distribution components is a canonical problem in modeling data with GMMs.
GMMs have been used for feature extraction from speech data, and have also been used extensively in object tracking of multiple objects, where the number of mixture components and their means predict object
Locations at each frame in a video sequence.