Distributed PCA and k-Means Clustering
نویسندگان
چکیده
This paper proposes a distributed PCA algorithm, with the theoretical guarantee that any good approximation solution on the projected data for k-means clustering is also a good approximation on the original data, while the projected dimension required is independent of the original dimension. When combined with the distributed coreset-based clustering approach in [3], this leads to an algorithm in which the number of vectors communicated is independent of the size and the dimension of the original data. Our experiment results demonstrate the effectiveness of the algorithm.
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