Sparse constrained projection approximation subspace tracking
Methodology
2018-11-27 v2
Abstract
In this paper we revisit the well-known constrained projection approximation subspace tracking algorithm (CPAST) and derive, for the first time, non-asymptotic error bounds. Furthermore, we introduce a novel sparse modification of CPAST which is able to exploit sparsity in the underlying covariance structure. We present a non-asymptotic analysis of the proposed algorithm and study its empirical performance on simulated and real data.
Cite
@article{arxiv.1810.09298,
title = {Sparse constrained projection approximation subspace tracking},
author = {Denis Belomestny and Ekaterina Krymova},
journal= {arXiv preprint arXiv:1810.09298},
year = {2018}
}