- 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