2020/06/26 by Jonathan von Schroeder, von Schroeder, Jonathan
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · Mathematics · #Advanced Chemical Sensor Technologies #Analytical Chemistry (journal) #Analytical Chemistry and Chromatography #Applications (stat.AP) #Artificial intelligence #Chatterjee #Chemistry #Chromatography #Computer science #Desorption #FOS: Computer and information sciences #Feature (linguistics) #Feature selection #MALDI imaging #Machine learning #Mass Spectrometry Techniques and Applications #Mass spectrometry #Mass spectrometry imaging #Materials science #Matrix-assisted laser desorption/ionization #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #Pattern recognition (psychology) #Stability (learning theory) #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2006.15077
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/06/26 · openalex publication_date 2020/06/26 · arxiv updated 2020/06/29 · openalex created_date 2022/07/26 · openalex updated_date 2026/08/06
This paper discusses an approach, based on the subsampling boostrap and FDR\ncontrol, to improve the stability of feature selection. It furthermore presents\nthe finite sample distribution of the correlation coefficient recently proposed\nby Chatterjee (2020) under the setting relevant for this paper. Finally an\napplication to matrix-assisted laser desorption/ionization (MALDI) imaging mass\nspectroscopy data is discussed.\n