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| 음이 아닌 행렬 분해(NMF)× | K-평균 군집화× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열≠ | Latent structure | Machine learning |
| 기원 연도≠ | 1999 | 1967 |
| 창시자≠ | Lee, D. D. & Seung, H. S. | MacQueen, J. |
| 유형≠ | Matrix decomposition with non-negativity constraints | Partitional clustering (centroid-based) |
| 원전≠ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ |
| 별칭≠ | NMF, NNMF, nonnegative matrix factorization, non-negative matrix approximation | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. |
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