Sketched Stochastic Dictionary Learning for large‐scale data and application to high‐throughput mass spectrometry

Abstract

Factorization of large data corpora has emerged as an essential technique to extract dictionaries (sets of patterns that are meaningful for sparse encoding). Following this line, we present a novel algorithm based on compressive learning theory. In this framework, the (arbitrarily large) dataset of interest is replaced by a fixed‐size sketch resulting from a random sampling of the data distribution characteristic function. We apply our algorithm to the extraction of chromatographic elution profiles in mass spectrometry data, where it demonstrates its efficiency and interest compared to other related algorithms.