方法对比
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| K-means聚类× | 自监督学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1967 (formalized 1982) | 2018–2020 |
| 提出者≠ | MacQueen, J. B.; Lloyd, S. P. | LeCun, Y. and community (formalized ~2018–2020) |
| 类型≠ | Partitional clustering | Representation learning paradigm |
| 开创性文献≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| 别名 | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 相关≠ | 4 | 3 |
| 摘要≠ | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
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