Gaussian process latent variable model

A Gaussian Process Latent Variable Model (GPLVM) is a statistical method used to simplify complex data by finding a smaller number of hidden factors, or "latent variables," that explain the data's structure. It assumes these hidden factors are related through a smooth, flexible function modeled by a Gaussian process, which can capture complex patterns without rigid assumptions. This helps us visualize, interpret, or generate new data by understanding the underlying relationships in high-dimensional data, such as images or sounds. Essentially, GPLVM provides a way to uncover meaningful low-dimensional insights from complex datasets.