Implementing Preprocessing and Classification Training for Gaussian Mixture Models in MATLAB

We will implement preprocessing and classification training for Gaussian Mixture Models (GMM) using MATLAB. This comprehensive approach involves several key stages: data preprocessing, feature extraction, model training, and performance evaluation. During data preprocessing, we will perform data cleaning, noise removal, and normalization using MATLAB functions like fillmissing for handling missing values, medfilt1 for median filtering, and normalize for data standardization to prepare the dataset for subsequent operations. For feature extraction, we will implement common dimensionality reduction techniques such as Principal Component Analysis (PCA) using pca function and Independent Component Analysis (ICA) through fastica algorithm to extract dominant features from the data. The model training phase involves GMM implementation using fitgmdist function for parameter estimation through Expectation-Maximization (EM) algorithm, where we'll specify the number of Gaussian components and covariance matrix type. Finally, during the evaluation stage, we will test the trained model's performance using techniques like cross-validation with crossval function, confusion matrix analysis via confusionmat, and computing accuracy metrics to ensure model reliability and classification accuracy. The implementation will include proper visualization using plot and scatter functions for result interpretation.