Iterative column subset selection

Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction is feature selection. Due to the impossibility of labelling many data sets, unsupervised approaches are frequently the only option. The colum...

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Bibliographic Details
Published in:Knowledge and information systems 2018, Vol.54 (1), p.65-94
Main Authors: Ordozgoiti, Bruno, Canaval, Sandra Gómez, Mozo, Alberto
Format: Article
Language:English
Subjects:
Algorithms
Computer Science
Data mining
Data Mining and Knowledge Discovery
Database Management
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Iterative algorithms
Iterative methods
Machine learning
Reduction
Regular Paper
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Summary:Dimensionality reduction is often a crucial step for the successful application of machine learning and data mining methods. One way to achieve said reduction is feature selection. Due to the impossibility of labelling many data sets, unsupervised approaches are frequently the only option. The column subset selection problem translates naturally to this purpose and has received considerable attention over the last few years, as it provides simple linear models for low-rank data reconstruction. Recently, it was empirically shown that an iterative algorithm, which can be implemented efficiently, provides better subsets than other state-of-the-art methods. In this paper, we describe this algorithm and provide a more in-depth analysis. We carry out numerous experiments to gain insights on its behaviour and derive a simple bound for the norm recovered by the resulting matrix. To the best of our knowledge, this is the first theoretical result of this kind for this algorithm.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-017-1115-4