Σύγκριση μεθόδων

	Ημι-επιβλεπόμενο Μοντέλο Θεμάτων LDA	Ημι-επιβλεπόμενο Μοντέλο Θέματος NMF
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2009	2001 (NMF); semi-supervised variants from ~2010s
Δημιουργός	Ramage, D.; Andrzejewski, D. et al.	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others
Τύπος	Semi-supervised probabilistic topic model	Matrix factorization with supervision
Θεμελιώδης πηγή	Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of EMNLP, 248–256.	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.
Εναλλακτικές ονομασίες	Labeled LDA, Seeded LDA, Constrained LDA, SS-LDA	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF
Συναφείς	6	6
Σύνοψη	Semi-supervised LDA extends standard Latent Dirichlet Allocation by incorporating a small amount of supervision — seed words, labeled documents, or must-link/cannot-link word constraints — to guide topic discovery toward semantically coherent, interpretable themes. It bridges unsupervised topic modeling and fully supervised text classification, making it especially valuable when full annotation is costly.	Semi-supervised Non-negative Matrix Factorization (NMF) Topic Model extends unsupervised NMF by incorporating user-provided seed words or label constraints to steer discovered topics toward domain-relevant themes. It factorizes a document-term matrix into interpretable non-negative components while respecting lexical priors, yielding coherent, application-aligned topics even from modest corpora.