josh.meanings.initializations.afk
Fast and Provably Good Seedings for k-Means is a paper by Olivier Bachem, Mario Lucic, S. Hamed Hassani, and Andreas Krause which introduces an improvement to the monte carlo markov chain approximation of k-means++ D^2 sampling. It accomplishes this by computing the D^2 sampling distribution with respect to the first cluster. This has the practical benefit of removing some of the assumptions, like choice of distance metric, which were imposed in the former framing. As such the name of this algorithm is assumption free k-mc^2. A savvy reader may note that by computing the D^2 sampling distribution as part of the steps this algorithm loses some of the theoretical advantages of the pure markov chain formulation. The paper argues that this is acceptable, because in practice computing the first D^2 sampling distribution ends up paying for itself by reducing the chain length necessary to get convergence guarantees.
Fast and Provably Good Seedings for k-Means is a paper by Olivier Bachem, Mario Lucic, S. Hamed Hassani, and Andreas Krause which introduces an improvement to the monte carlo markov chain approximation of k-means++ D^2 sampling. It accomplishes this by computing the D^2 sampling distribution with respect to the first cluster. This has the practical benefit of removing some of the assumptions, like choice of distance metric, which were imposed in the former framing. As such the name of this algorithm is assumption free k-mc^2. A savvy reader may note that by computing the D^2 sampling distribution as part of the steps this algorithm loses some of the theoretical advantages of the pure markov chain formulation. The paper argues that this is acceptable, because in practice computing the first D^2 sampling distribution ends up paying for itself by reducing the chain length necessary to get convergence guarantees.
make-weight-fnclj
(make-weight-fn distance-fn clusters)Create a function which computes the weight of a point given the current set of clusters.
Create a function which computes the weight of a point given the current set of clusters.
samples-neededclj
(samples-needed k m)Returns the number of samples needed to do monte carlo sampling.
Returns the number of samples needed to do monte carlo sampling.
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