mixture-model

I have applied gaussmix function in voicebox MATLAB tools to calculate GMM. However, the code gives me error when I run it for 512 GMM components. No_of_Clusters = 512; No_of_Iterations = 10; [m_ubm1,v_ubm1,w_ubm1]=gaussmix(feature,[],No_of_Iterations,No_of_Clusters); Error using * Inner matrix dimensions must agree. Error in gaussmix (line 256) pk=px*wt; % pk(k,1) effective number of data points for each mixture (could be zero due to underflow) I need 1024 or 2048 Mixtures for Universal Background Model (UBM) construction. Could anyone give me matlab code to calculate GMM for big number of

I am attempting to do automatic image segmentation of the different regions of a 2D MR image based on pixel intensity values. The first step is implementing a Gaussian Mixture Model on the image's histogram. I need to plot the resulting gaussian obtained from the score_samples method onto the histogram. I have tried following the code in the answer to ( Understanding Gaussian Mixture Models ). However, the resulting gaussian fails to match the histogram at all. How do I get the gaussian to match the histogram? import numpy as np import cv2 import matplotlib.pyplot as plt from sklearn.mixture

I have a sample of 1m records obtained from my original data. (For your reference, you may use this dummy data that may generate approximately similar distribution b <- data.frame(matrix(rnorm(2000000, mean=c(8,17), sd=2))) c <- b[sample(nrow(b), 1000000), ] ) I believed the histogram to be a mixture of two log-normal distributions and I tried to fit the summed distributions using EM algorithm using the following code: install.packages("mixtools") lib(mixtools) #line below returns EM output of type mixEM[] for mixture of normal distributions c1 <- normalmixEM(c, lambda=NULL, mu=NULL, sigma

I'm trying to understand GMM by reading the sources available online. I have achieved clustering using K-Means and was seeing how GMM would compare to K-means. Here is what I have understood, please let me know if my concept is wrong: GMM is like KNN, in the sense that clustering is achieved in both cases. But in GMM each cluster has their own independent mean and covariance. Furthermore k-means performs hard assignments of data points to clusters whereas in GMM we get a collection of independant gaussian distributions, and for each data point we have a probability that it belongs to one of