Gaussian mixture models (GMM) is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It is a clustering algorithm and it is one of the most commonly used algorithms for clustering.
GMM is a type of unsupervised learning algorithm and can be used for various applications ranging from classification, data clustering and density estimation. The main goal of this type of model is to determine the underlying structure of a dataset by finding hidden patterns in the data points. It can be used to identify homogeneous clusters within a data-set.
The Gaussian mixture model is based on the idea that a data-set can be described as a mix of multiple Gaussian distributions, each with their own parameters (mean, variance, etc.) The model assigns each data point to a component Gaussian, and the parameters of the component Gaussian are then estimated based on the data.
In GMM, the component Gaussians are combined in a weighted sum, with each component contributing some weight to the total mixture. The idea is that the weights will capture the relative likelihood that each component contributes to the total data distribution.
GMM is a well-known and widely used algorithm in many fields including computer vision, signal processing, image segmentation, data mining, and more. GMM is easy to implement and it is able to model complex data distributions with relatively few parameters.
Despite its advantages, GMM has some drawbacks. For example, it only works for data that is well represented by a mixture of Gaussian distributions, and it may have difficulty finding clusters in more complex data. Additionally, the parameters of the Gaussians can be sensitive to the choice of initialization, which can be difficult to optimize.
