Salīdzināt metodes

	Generatīvais modelis, kas balstīts uz skaitlisko vērtību (rezultātu)	Primārā komponentu analīze
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019	2002
Autors	Song, Y. & Ermon, S.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tips	Score-based generative model (SDE framework)	Unsupervised dimensionality reduction
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	3
Kopsavilkums	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.