Greed is good: correspondence recovery for unlabeled linear regression

Hang Zhang, Ping Li
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:2509-2518, 2023.

Abstract

We consider the unlabeled linear regression reading as Y=Π∗XB∗+W, where Π∗,B∗ and W represents missing (or incomplete) correspondence information, signals, and additive noise, respectively. Our goal is to perform data alignment between Y and X, or equivalently, reconstruct the correspondence information encoded by Π∗. Based on whether signal B∗ is given a prior, we separately propose two greedy-selection-based estimators, which both reach the mini-max optimality. Compared with previous works, our work (i) supports partial recovery of the correspondence information; and (ii) applies to a general matrix family rather than the permutation matrices, to put more specifically, selection matrices, where multiple rows of X can correspond to the same row in Y. Moreover, numerical experiments are provided to corroborate our claims.

Cite this Paper

BibTeX
@InProceedings{pmlr-v216-zhang23e, title = {Greed is good: correspondence recovery for unlabeled linear regression}, author = {Zhang, Hang and Li, Ping}, booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}, pages = {2509--2518}, year = {2023}, editor = {Evans, Robin J. and Shpitser, Ilya}, volume = {216}, series = {Proceedings of Machine Learning Research}, month = {31 Jul--04 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v216/zhang23e/zhang23e.pdf}, url = {https://proceedings.mlr.press/v216/zhang23e.html}, abstract = {We consider the unlabeled linear regression reading as $\mathbf{Y} = \mathbf{\Pi}^{*}\mathbf{X}\mathbf{B}^* + \mathbf{W}$, where $\mathbf{\Pi}^{*}, \mathbf{B}^*$ and $\mathbf{W}$ represents missing (or incomplete) correspondence information, signals, and additive noise, respectively. Our goal is to perform data alignment between $\mathbf{Y}$ and $\mathbf{X}$, or equivalently, reconstruct the correspondence information encoded by $\mathbf{\Pi}^*$. Based on whether signal $\mathbf{B}^*$ is given a prior, we separately propose two greedy-selection-based estimators, which both reach the mini-max optimality. Compared with previous works, our work $(i)$ supports partial recovery of the correspondence information; and $(ii)$ applies to a general matrix family rather than the permutation matrices, to put more specifically, selection matrices, where multiple rows of $\mathbf{X}$ can correspond to the same row in $\mathbf{Y}$. Moreover, numerical experiments are provided to corroborate our claims.} }
Endnote
%0 Conference Paper %T Greed is good: correspondence recovery for unlabeled linear regression %A Hang Zhang %A Ping Li %B Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2023 %E Robin J. Evans %E Ilya Shpitser %F pmlr-v216-zhang23e %I PMLR %P 2509--2518 %U https://proceedings.mlr.press/v216/zhang23e.html %V 216 %X We consider the unlabeled linear regression reading as $\mathbf{Y} = \mathbf{\Pi}^{*}\mathbf{X}\mathbf{B}^* + \mathbf{W}$, where $\mathbf{\Pi}^{*}, \mathbf{B}^*$ and $\mathbf{W}$ represents missing (or incomplete) correspondence information, signals, and additive noise, respectively. Our goal is to perform data alignment between $\mathbf{Y}$ and $\mathbf{X}$, or equivalently, reconstruct the correspondence information encoded by $\mathbf{\Pi}^*$. Based on whether signal $\mathbf{B}^*$ is given a prior, we separately propose two greedy-selection-based estimators, which both reach the mini-max optimality. Compared with previous works, our work $(i)$ supports partial recovery of the correspondence information; and $(ii)$ applies to a general matrix family rather than the permutation matrices, to put more specifically, selection matrices, where multiple rows of $\mathbf{X}$ can correspond to the same row in $\mathbf{Y}$. Moreover, numerical experiments are provided to corroborate our claims.
APA
Zhang, H. & Li, P.. (2023). Greed is good: correspondence recovery for unlabeled linear regression. Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 216:2509-2518 Available from https://proceedings.mlr.press/v216/zhang23e.html.

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