Efficient Calculation of Polynomial Features on Sparse Matrices

Andrew Nystrom, John Hughes

Invalid Date (modified: Nov 04, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: We provide an algorithm for polynomial feature expansion that both operates on and produces a compressed sparse row matrix without any densification. For a vector of dimension D, density d, and degree k the algorithm has time complexity O(d^k * D^k) where k is the polynomial-feature order; this is an improvement by a factor d^k over the standard method.
  • TL;DR: An algorithm to perform polynomial expansions on CSR matrices that scales with matrix density polynomially.
  • Keywords: Supervised Learning
  • Conflicts: google.com, ed.ac.uk, cs.brown.edu, umn.edu
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