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| Multimodal NMF Topic Model× | Neatūru matricas faktorizācija (NMF)× | |
|---|---|---|
| Nozare≠ | Dziļā mācīšanās | Mašīnmācīšanās |
| Saime≠ | Machine learning | Latent structure |
| Izcelsmes gads≠ | 2010s | 1999 |
| Autors≠ | Lee & Seung (NMF); multimodal extensions by various authors (~2010s) | Lee, D. D. & Seung, H. S. |
| Tips≠ | Multimodal topic model (NMF-based) | Matrix decomposition with non-negativity constraints |
| Pirmavots≠ | 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 ↗ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ |
| Citi nosaukumi≠ | Multimodal NMF, Multi-view NMF topic model, Joint NMF topic model, MM-NMF | NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation |
| Saistītās≠ | 2 | 4 |
| Kopsavilkums≠ | 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. | Non-negative Matrix Factorization (NMF) is a family of algorithms, introduced by Lee and Seung in their landmark 1999 Nature paper, that decomposes a non-negative data matrix V into the product of two lower-rank non-negative matrices W (basis components) and H (encoding coefficients). Unlike PCA or SVD, the non-negativity constraint forces the algorithm to learn strictly additive, parts-based representations, making the factors directly interpretable as building blocks of the original data. |
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