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| Modèle thématique par factorisation matricielle non négative semi-supervisée× | Modélisation par sujets× | |
|---|---|---|
| Domaine | Apprentissage profond | Apprentissage profond |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2001 (NMF); semi-supervised variants from ~2010s | 1999–2003 |
| Auteur d'origine≠ | 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) |
| Type≠ | Matrix factorization with supervision | Unsupervised generative probabilistic model |
| Source fondatrice≠ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Alias | SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | 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. |
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