Salīdzināt metodes

	Neatūru matricas faktorizācija (NMF)	Pikseļu bāzēta attēlu klasifikācija
Nozare	Mašīnmācīšanās	Tālizpēte
Saime	Latent structure	Machine learning
Izcelsmes gads	1999	2007
Autors	Lee, D. D. & Seung, H. S.	Remote-sensing classification literature
Tips	Matrix decomposition with non-negativity constraints	Supervised/unsupervised spectral image classification
Pirmavots	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.	Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.
Citi nosaukumi	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
Saistītās	4	2
Kopsavilkums	Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data.	Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels.