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Random Projections for k-means Clustering

2020-01-06 | |  66 |   39 |   0

Abstract

This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix 图片.png can be projected into 图片.png dimensions, for any 图片.png in 图片.pngtime, such that with constant probability the optimal k-partition of the point set is preserved within a factor of 图片.png The projection  is done by post-multiplying A with a d × t random matrix R having entries 图片.pngwith equal probability. A numerical implementation of our technique and experiments on a large face images dataset verify the speed and the accuracy of our theoretical results.

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