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Published in:
2015 | OriginalPaper | Chapter
Random Projection Towards the Baire Metric for High Dimensional Clustering
Authors : Fionn Murtagh, Pedro Contreras
Published in: Statistical Learning and Data Sciences
Publisher: Springer International Publishing
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For high dimensional clustering and proximity finding, also referred to as high dimension and low sample size data, we use random projection with the following principle. With the greater probability of close-to-orthogonal projections, compared to orthogonal projections, we can use rank order sensitivity of projected values. Our Baire metric, divisive hierarchical clustering, is of linear computation time.