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| 半教師ありNMFトピックモデル× | NMFトピックモデル× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2001 (NMF); semi-supervised variants from ~2010s | 1999 |
| 提唱者≠ | Lee & Seung (NMF); semi-supervised extensions by Jagarlamudi et al. and others | Lee, D. D. & Seung, H. S. |
| 種類≠ | Matrix factorization with supervision | Matrix factorization / unsupervised topic model |
| 原典≠ | Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562. link ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| 別名 | SS-NMF, guided NMF, constrained NMF topic model, seed-guided NMF | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model |
| 関連≠ | 6 | 4 |
| 概要≠ | 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. | Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics. |
| ScholarGateデータセット ↗ |
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