The expectation-maximization (EM) algorithm is an elegant algorithm that maximizes the likelihood function for problems with latent or hidden variables. As from the name itself it could primarily be ...

A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing ...

Abstract: We introduce a new class of “maximization-expectation” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation ...

Abstract: The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications press for the processing of multiple temporal hyperspectral images. In this work, ...

In the original published article, there were typographical errors in mathematical formulas (Equations 58, 59, 73, and 74). The equations were derived and implemented correctly in the computer program ...

In this work, we propose EMDiffusion, an expectation-maximization (EM) approach to train diffusion models from corrupted observations. Our method alternates between reconstructing clean images from ...