Margin-constrained Random Projections And Very Sparse Random Projections

  title={Margin-constrained Random Projections And Very Sparse Random Projections},
  author={Ping Li and Trevor J. Hastie},
Abstract We1 propose methods for improving both the accuracy and efficiency of random projections, the popular dimension reduction technique in machine learning and data mining, particularly useful for estimating pairwise distances. Let A ∈ Rn×D be our n points in D dimensions. This method multiplies A by a random matrix R ∈ RD×k, reducing the D dimensions down to just k . R typically consists of i.i.d. entries in N(0, 1). The cost of the projection mapping is O(nDk). This study proposes an… CONTINUE READING