Vertaile menetelmiä

	Ei-negatiivinen matriisihajotelma (NMF)	Pikselipohjainen kuvien luokittelu
Tieteenala	Koneoppiminen	Kaukokartoitus
Menetelmäperhe	Latent structure	Machine learning
Syntyvuosi	1999	2007
Kehittäjä	Lee, D. D. & Seung, H. S.	Remote-sensing classification literature
Tyyppi	Matrix decomposition with non-negativity constraints	Supervised/unsupervised spectral image classification
Alkuperäislähde	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.
Rinnakkaisnimet	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
Liittyvät	4	2
Tiivistelmä	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.