Sketched Stochastic Dictionary Learning for large‐scale data and application to high‐throughput mass spectrometry. (20th August 2021)

• Record Type: Journal Article
• Authors: Permiakova, Olga; Burger, Thomas
• Is Part Of: Statistical analysis and data mining. Volume 15:Number 1(2022)
• Page Start: 43
• Page End: 56
• Publication Date: 2021-08-20
• Subjects: compressive statistical learning -- computational mass spectrometry -- dictionary learning -- matrix factorization -- Nesterov accelerated gradient descent -- stochastic gradient descent; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11542
• Languages: English
• ISSNs: 1932-1864

Abstract

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.