Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:1033-1041, 2015.
Abstract
Column subset selection of massive data matrices has found numerous applications in real-world data systems. In this paper, we propose and analyze two sampling based algorithms for column subset selection without access to the complete input matrix. To our knowledge, these are the first algorithms for column subset selection with missing data that are provably correct. The proposed methods work for row/column coherent matrices by employing the idea of adaptive sampling. Furthermore, when the input matrix has a noisy low-rank structure, one algorithm enjoys a relative error bound.
@InProceedings{pmlr-v38-wang15c,
title = {{Column Subset Selection with Missing Data via Active Sampling}},
author = {Yining Wang and Aarti Singh},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
pages = {1033--1041},
year = {2015},
editor = {Guy Lebanon and S. V. N. Vishwanathan},
volume = {38},
series = {Proceedings of Machine Learning Research},
address = {San Diego, California, USA},
month = {09--12 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v38/wang15c.pdf},
url = {http://proceedings.mlr.press/v38/wang15c.html},
abstract = {Column subset selection of massive data matrices has found numerous applications in real-world data systems. In this paper, we propose and analyze two sampling based algorithms for column subset selection without access to the complete input matrix. To our knowledge, these are the first algorithms for column subset selection with missing data that are provably correct. The proposed methods work for row/column coherent matrices by employing the idea of adaptive sampling. Furthermore, when the input matrix has a noisy low-rank structure, one algorithm enjoys a relative error bound.}
}
%0 Conference Paper
%T Column Subset Selection with Missing Data via Active Sampling
%A Yining Wang
%A Aarti Singh
%B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics
%C Proceedings of Machine Learning Research
%D 2015
%E Guy Lebanon
%E S. V. N. Vishwanathan
%F pmlr-v38-wang15c
%I PMLR
%J Proceedings of Machine Learning Research
%P 1033--1041
%U http://proceedings.mlr.press
%V 38
%W PMLR
%X Column subset selection of massive data matrices has found numerous applications in real-world data systems. In this paper, we propose and analyze two sampling based algorithms for column subset selection without access to the complete input matrix. To our knowledge, these are the first algorithms for column subset selection with missing data that are provably correct. The proposed methods work for row/column coherent matrices by employing the idea of adaptive sampling. Furthermore, when the input matrix has a noisy low-rank structure, one algorithm enjoys a relative error bound.
TY - CPAPER
TI - Column Subset Selection with Missing Data via Active Sampling
AU - Yining Wang
AU - Aarti Singh
BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics
PY - 2015/02/21
DA - 2015/02/21
ED - Guy Lebanon
ED - S. V. N. Vishwanathan
ID - pmlr-v38-wang15c
PB - PMLR
SP - 1033
DP - PMLR
EP - 1041
L1 - http://proceedings.mlr.press/v38/wang15c.pdf
UR - http://proceedings.mlr.press/v38/wang15c.html
AB - Column subset selection of massive data matrices has found numerous applications in real-world data systems. In this paper, we propose and analyze two sampling based algorithms for column subset selection without access to the complete input matrix. To our knowledge, these are the first algorithms for column subset selection with missing data that are provably correct. The proposed methods work for row/column coherent matrices by employing the idea of adaptive sampling. Furthermore, when the input matrix has a noisy low-rank structure, one algorithm enjoys a relative error bound.
ER -
Wang, Y. & Singh, A.. (2015). Column Subset Selection with Missing Data via Active Sampling. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in PMLR 38:1033-1041
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