So sánh phương pháp

	Mô hình sinh sinh theo điểm số	Phân tích thành phần chính
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2019	2002
Người khởi xướng	Song, Y. & Ermon, S.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Loại	Score-based generative model (SDE framework)	Unsupervised dimensionality reduction
Công trình gốc	Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Tên gọi khác	Skor Tabanlı Üretici Model (Score-Based / SDE), score-based diffusion, SDE-based generative model, score SDE	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Liên quan	5	3
Tóm tắt	A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.	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.