A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is `application/pdf`

.

##
###
Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations

2018
*
International Symposium on Mathematical Foundations of Computer Science
*

Low rank approximation of matrices is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns, and provides concise representations for the data. Research on low rank approximation usually focuses on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrices. Here we are given a d × n binary matrix A and a small integer k < d.

doi:10.4230/lipics.mfcs.2018.41
dblp:conf/mfcs/DanHJ0Z18
fatcat:5dkc3yjjmbdnrpsaxgtblh5i6u