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Compressed Counting Meets Compressed Sensing

Published on Feb 4, 20253209 Views

Ping Li

Compressed sensing (sparse signal recovery) has been a popular and important research topic in recent years. By observing that natural signals (e.g., images or network data) are often nonnegative, we

COLT 2014 - Barcelona

Related categories

Computational Learning TheoryStatistical LearningSupervised LearningUnsupervised LearningOn-line Learning

Presentation

Compressed Counting Meets Compressed Sensing00:00
New Direction on Compressed Sensing (Sparse Recovery)00:00
A Simple Interesting Story about Compressed Sensing01:05
A 3-Iteration 3-Measurement Scheme - 101:47
A 3-Iteration 3-Measurement Scheme - 202:55
A 3-Iteration 3-Measurement Scheme - 304:10
A 3-Iteration 3-Measurement Scheme - 404:39
Realization of the Ideal Design05:06
Ratio of Two Independent α-Stable Variables05:59
Advantages of the New Compressed Sensing Framework06:48
Maximally-Skewed α-Stable Distribution08:19
The Recovery Algorithm09:13
Sample Complexity of One-Scan Technique09:59
The Constant Cα11:04
Comparison with Count-Min Sketch11:05
Experiments - 112:16
Experiments - 214:17
Experiments - 414:35
Experiments - 314:44
Extensions14:53
Sign Recovery by One Scan 1-Bit Compressed Sensing15:55
Summary of Contributions on Compressed Sensing15:58