- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages / Article No.
SubjectQuantization; Convex optimization; Matrix completion; Robust principal component analysis
Organisational unit09695 - Studer, Christoph / Studer, Christoph
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