k-means++¶
Arthur, D., & Vassilvitskii, S. (2007). k-means++: The Advantages of Careful Seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 1027-1035.
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Primes¶
- Local Optimum
- The prime's escape analysis is borne out by the standard remedy: because the trapped competence cannot escape by more of the same descent, practitioners use a different kind of move — random restarts (k-means++ seeding samples diverse starting basins), which covers more basins, exactly the prime's "diversity in search" inference.
This sourceSeeding that samples diverse starting basins to escape initialization-dependent local optima.
- The prime's escape analysis is borne out by the standard remedy: because the trapped competence cannot escape by more of the same descent, practitioners use a different kind of move — random restarts (k-means++ seeding samples diverse starting basins), which covers more basins, exactly the prime's "diversity in search" inference.
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