Explore MATLAB source code curated for "EM算法" with clean implementations, documentation, and examples.
This MATLAB implementation solves parameter estimation for Gaussian Mixture Models using the Expectation-Maximization (EM) algorithm. The program modularly separates mean, covariance, and weight estimation into independent functions saved as .M files. The main execution point is through main.m, with sample data provided in spreadsheet format for immediate testing and customization.
Application Context: In statistical computing, the Expectation-Maximization (EM) algorithm is used to find maximum likelihood or maximum a posteriori estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is frequently applied in machine learning and computer vision for data clustering tasks. Key Technology: The EM algorithm iterates through two alternating steps: - E-step (Expectation): Computes the expected value of the log-likelihood function using current estimates of hidden variables - M-step (Maximization): Finds parameters that maximize the expected log-likelihood computed in the E-step Parameters estimated in the M-step are reused in the next E-step, creating an iterative convergence process.
Reliable EM algorithm implementation sourced from MATLAB's official repository, providing robust data processing capabilities.
A MATLAB implementation of the EM algorithm that everyone is searching for, featuring complete probabilistic modeling and iterative optimization capabilities
MATLAB code for EM algorithm implementation. The extracted package can be directly executed in MATLAB environment to demonstrate expectation-maximization for statistical parameter estimation and clustering applications.
MATLAB-based implementation of the Expectation-Maximization algorithm for estimating means, variances, and weights in Gaussian Mixture Models, featuring iterative optimization and parameter estimation capabilities.
A MATLAB implementation of the GMM algorithm featuring sample demonstrations, utilizing EM algorithm for GMM parameter estimation with code-level implementation details.
This approach utilizes the Expectation-Maximization (EM) algorithm for parameter training in wavelet domain Hidden Markov Models (HMMs), demonstrating improved efficiency in training time compared to alternative methods. The implementation involves iterative estimation of latent state probabilities and optimization of model parameters through maximum likelihood estimation.
This paper presents an effective implementation of Gaussian Mixture Models (GMM), a classic speaker recognition algorithm, using the Expectation-Maximization (EM) algorithm. The study primarily simulates GMM's noise robustness performance under various acoustic environments, yielding valuable insights for practical applications. Key implementation aspects include parameter initialization strategies and convergence criteria for the EM iteration process.
This uploaded source code provides a practical programming example demonstrating the implementation of the EM (Expectation-Maximization) algorithm.