পদ্ধতির তুলনা করুন

	Semi-supervised NMF Topic Model	টপিক মডেলিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001 (NMF); semi-supervised variants from ~2010s	1999–2003
প্রবর্তক	Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
ধরন	Matrix factorization with supervision	Unsupervised generative probabilistic model
মৌলিক উৎস	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
অপর নাম	SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
সম্পর্কিত	6	5
সারসংক্ষেপ	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.