Salīdzināt metodes

	Autoencoder	K-means klasterizācija
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006	1967 (formalized 1982)
Autors	Hinton, G.E. & Salakhutdinov, R.R.	MacQueen, J. B.; Lloyd, S. P.
Tips	Neural network (encoder-decoder)	Partitional clustering
Pirmavots	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Citi nosaukumi	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Saistītās	4	4
Kopsavilkums	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.