方法对比
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| 自监督 K-均值× | 在线K均值聚类 (Online K-means)× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 | 1967 (online update rule); 2010 (mini-batch variant) |
| 提出者≠ | Caron, M. et al. (DeepCluster framework) | MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant) |
| 类型≠ | Self-supervised clustering | Unsupervised clustering (online/streaming) |
| 开创性文献≠ | Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149. link ↗ | MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press. link ↗ |
| 别名 | self-supervised clustering with K-means, deep clustering with K-means, unsupervised K-means with pseudo-labels, SSL K-means | sequential k-means, streaming k-means, incremental k-means, online clustering |
| 相关≠ | 5 | 4 |
| 摘要≠ | Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth. | Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical. |
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