2008/01/23 by Peter Clifford, Clifford, Peter, Ioana A. Cosma +1
Computer Science · Engineering · Mathematics · #Computation (stat.CO) #Data Management and Algorithms #FOS: Computer and information sciences #Face and Expression Recognition #Sparse and Compressive Sensing Techniques #stat.CO
paper · pdf · doi:10.48550/arxiv.0801.3559
8 pages, 3 figures, submitted to COMPSTAT2008
arxiv created 2008/01/23 · openalex publication_date 2008/01/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, large high-dimensional data sets have become commonplace in a wide range of applications in science and commerce. Techniques for dimension reduction are of primary concern in statistical analysis. Projection methods play an important role. We investigate the use of projection algorithms that exploit properties of the alpha-stable distributions. We show that lalpha distances and quasi-distances can be recovered from random projections with full statistical efficiency by L-estimation. The computational requirements of our algorithm are modest; after a once-and-for-all calculation to determine an array of length k, the algorithm runs in O(k) time for each distance, where k is the reduced dimension of the projection.