Gaussian Mixture Model (GMM)

Gaussian Mixture Model (GMM) a model-based probabilistic method that assumes data points are generated from a mixture of several Gaussian distributions with unknown parameters. It uses the Expectation Maximization (EM) algorithm to update the parameters iteratively in order to optimize the log-likelihood of the data until convergence.

PhyloClustering.gmm_label — Function

gmm_label(tree::AbstractMatrix{<:Real}, n::Int64; method::Symbol=:kmeans, kind::Symbol=:diag)

Get predicted labels from Gaussian mixture model for a group of phylogenetic trees.

Arguments

tree: a B * N tree Matrix (each column of tree Matrix is a B-dimensional tree in bipartiton format).
n: the number of clusters.
method(defaults to :kmeans): intialization method to find n starting centers: * :kmeans: use K-means clustering from Clustering.jl to initialize with n centers. * :split: initialize a single Gaussian with tree and subsequently splitting the Gaussians followed by retraining using the EM algorithm until n Gaussians are obtained.
kind(defaults to :diag): covariance type, :diag or :full.

Output

A Tuple{Vector{Int64}, Vector{Int64}} where the first Vector contains predicted labels for each tree based on the posterior probability and the second Vector contain predicted labels for each tree based on the Log Likelihood.

source

Reference

The implementation of GMM is provided by GaussianMixtures.jl.