Binary and Multi-Bit Coding for Stable Random Projections

Ping Li
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1430-1438, 2017.

Abstract

The recent work [17] developed a 1-bit compressed sensing (CS) algorithm based on α-stable random projections. Although the work in [17] showed that the method is a strong competitor of other existing 1-bit algorithms, the procedure requires knowing K, the sparsity. Note that K is the l_0 norm of the signal. Other existing 1-bit CS algorithms require the l_2 norm of the signal. In this paper, we develop an estimation procedure for the l_α norm of the signal, where 0<α\leq2 from binary or multi-bit measurements. We demonstrate that using a simple closed-form estimator with merely 1-bit information does not result in a significant loss of accuracy if the parameter is chosen appropriately. Theoretical tail bounds are also provided. Using 2 or more bits per measurement reduces the variance and importantly, stabilizes the estimate so that the variance is not too sensitive to chosen parameters.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-li17c, title = {{Binary and Multi-Bit Coding for Stable Random Projections}}, author = {Li, Ping}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {1430--1438}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/li17c/li17c.pdf}, url = {https://proceedings.mlr.press/v54/li17c.html}, abstract = {The recent work [17] developed a 1-bit compressed sensing (CS) algorithm based on $α$-stable random projections. Although the work in [17] showed that the method is a strong competitor of other existing 1-bit algorithms, the procedure requires knowing $K$, the sparsity. Note that $K$ is the $l_0$ norm of the signal. Other existing 1-bit CS algorithms require the $l_2$ norm of the signal. In this paper, we develop an estimation procedure for the $l_α$ norm of the signal, where $0<α\leq2$ from binary or multi-bit measurements. We demonstrate that using a simple closed-form estimator with merely 1-bit information does not result in a significant loss of accuracy if the parameter is chosen appropriately. Theoretical tail bounds are also provided. Using 2 or more bits per measurement reduces the variance and importantly, stabilizes the estimate so that the variance is not too sensitive to chosen parameters.} }
Endnote
%0 Conference Paper %T Binary and Multi-Bit Coding for Stable Random Projections %A Ping Li %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-li17c %I PMLR %P 1430--1438 %U https://proceedings.mlr.press/v54/li17c.html %V 54 %X The recent work [17] developed a 1-bit compressed sensing (CS) algorithm based on $α$-stable random projections. Although the work in [17] showed that the method is a strong competitor of other existing 1-bit algorithms, the procedure requires knowing $K$, the sparsity. Note that $K$ is the $l_0$ norm of the signal. Other existing 1-bit CS algorithms require the $l_2$ norm of the signal. In this paper, we develop an estimation procedure for the $l_α$ norm of the signal, where $0<α\leq2$ from binary or multi-bit measurements. We demonstrate that using a simple closed-form estimator with merely 1-bit information does not result in a significant loss of accuracy if the parameter is chosen appropriately. Theoretical tail bounds are also provided. Using 2 or more bits per measurement reduces the variance and importantly, stabilizes the estimate so that the variance is not too sensitive to chosen parameters.
APA
Li, P.. (2017). Binary and Multi-Bit Coding for Stable Random Projections. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:1430-1438 Available from https://proceedings.mlr.press/v54/li17c.html.

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