So sánh phương pháp

	Bộ tự mã hóa biến phân	Bộ tự mã hóa	Phân tích thành phần chính
Lĩnh vực	Học sâu	Học sâu	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2014	2006	2002
Người khởi xướng	Kingma, D. P. & Welling, M.	Hinton, G.E. & Salakhutdinov, R.R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Loại	Deep generative latent-variable model (encoder–decoder)	Neural network (encoder-decoder)	Unsupervised dimensionality reduction
Công trình gốc	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Tên gọi khác	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Liên quan	5	4	3
Tóm tắt	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.