Linganisha mbinu

	Muundo wa Uzalishaji unaotegemea Alama	Uchanganuzi wa Vipengele Vikuu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2019	2002
Mwanzilishi	Song, Y. & Ermon, S.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Aina	Score-based generative model (SDE framework)	Unsupervised dimensionality reduction
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	3
Muhtasari	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.