@inproceedings{88535e7aab544a5eb26338afbda90d00,
title = "Matrix recovery from quantized and corrupted measurements",
abstract = "This paper deals with the recovery of an unknown, low-rank matrix from quantized and (possibly) corrupted measurements of a subset of its entries. We develop statistical models and corresponding (multi-)convex optimization algorithms for quantized matrix completion (Q-MC) and quantized robust principal component analysis (Q-RPCA). In order to take into account the quantized nature of the available data, we jointly learn the underlying quantization bin boundaries and recover the low-rank matrix, while removing potential (sparse) corruptions. Experimental results on synthetic and two real-world collaborative filtering datasets demonstrate that directly operating with the quantized measurements - rather than treating them as real values - results in (often significantly) lower recovery error if the number of quantization bins is less than about 10.",
keywords = "convex optimization, matrix completion, Quantization, robust principal component analysis",
author = "Lan, {Andrew S.} and Christoph Studer and Baraniuk, {Richard G.}",
year = "2014",
doi = "10.1109/ICASSP.2014.6854548",
language = "English (US)",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4973--4977",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
address = "United States",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}