পদ্ধতির তুলনা করুন

	Non-negative Matrix Factorization (NMF)	পিক্সেল-ভিত্তিক চিত্র শ্রেণিবিন্যাস
ক্ষেত্র	যন্ত্র শিখন	দূর অনুধাবন
পরিবার	Latent structure	Machine learning
উদ্ভবের বছর	1999	2007
প্রবর্তক	Lee, D. D. & Seung, H. S.	Remote-sensing classification literature
ধরন	Matrix decomposition with non-negativity constraints	Supervised/unsupervised spectral image classification
মৌলিক উৎস	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.
অপর নাম	NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
সম্পর্কিত	4	2
সারসংক্ষেপ	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.