Tag Archives: Quantization recovery

Compressed Labeling: An important extension of Hamming Compressed Sensing; at NIPS now

We are just informed that our submission “Compressed Labeling (CL) on Distilled Labelsets (DL) for Multi-label Learning” is accepted by Machine Learning Journal (Springer). Online first PDF can be downloaded here. CL is an important application and extension of Hamming … Continue reading

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Hamming Compressed Sensing-recovering k-bit quantization from 1-bit measurements with linear non-iterative algorithm

We developed a new compressed sensing type signal acquisition paradigm called “Hamming Compressed Sensing (HCS)” to recover signal’s k-bit quantization rather than itself. Directly recovering quantization is much more preferred in practical digital systems. HCS provides a linear, non-iterative quantization … Continue reading

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