Vertaile menetelmiä
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| Monimodaalinen NMF-aihemalli× | Latent Dirichlet Allocation (LDA)× | |
|---|---|---|
| Tieteenala≠ | Syväoppiminen | Koneoppiminen |
| Menetelmäperhe≠ | Machine learning | Latent structure |
| Syntyvuosi≠ | 2010s | 2003 |
| Kehittäjä≠ | Lee & Seung (NMF); multimodal extensions by various authors (~2010s) | Blei, D. M.; Ng, A. Y.; Jordan, M. I. |
| Tyyppi≠ | Multimodal topic model (NMF-based) | Generative probabilistic topic model (three-level hierarchical Bayesian) |
| Alkuperäislähde≠ | Cai, D., He, X., Han, J., & Huang, T. S. (2011). Graph regularized NMF. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1548–1560. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. DOI ↗ |
| Rinnakkaisnimet≠ | Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF | LDA, topic model, Blei-Ng-Jordan model, probabilistic topic modeling |
| Liittyvät≠ | 2 | 3 |
| Tiivistelmä≠ | Multimodal NMF Topic Model extends Non-negative Matrix Factorization to simultaneously discover latent topics across multiple data modalities — such as text and images — by enforcing shared or aligned low-rank factor matrices. It uncovers coherent, interpretable topics that jointly explain patterns in both textual and visual (or other) feature spaces. | Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing. |
| ScholarGateAineisto ↗ |
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