So sánh phương pháp

	Mô hình Hỗn hợp Gaussian	Phân tích thành phần chính	SHAP (SHapley Additive exPlanations)
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	1977	2002	2017
Người khởi xướng	Dempster, Laird & Rubin (EM algorithm)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Lundberg, S.M. & Lee, S.-I.
Loại	Probabilistic (soft) clustering — mixture model	Unsupervised dimensionality reduction	Model-explanation method (Shapley-value attribution)
Công trình gốc	Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.
Tên gọi khác	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	SHAP Değerleri (Model Açıklanabilirlik), Shapley additive explanations, SHAP values, model explainability
Liên quan	4	3	5
Tóm tắt	A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).