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Conjugate gradient iterative hard thresholding for compressed sensing and matrix completion
Published on 2014-10-292436 Views
Compressed sensing and matrix completion are techniques by which simplicity in data can be exploited for more efficient data acquisition. For instance, if a matrix is known to be (approximately) low r
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Presentation
CGIHT for compressed sensing and matrix completion00:00
The simplicity of large data sets00:48
Untitled02:32
Compressed Sensing03:05
Matrix Completion03:26
Explicit search for simple solution from04:55
Convex relaxations06:01
Optimal order recovery - sampling theorems06:37
CS: ` 1 decoder08:13
MC: Schatten-1 decoder08:49
Three prototypical IHT algorithms for CS16:34
Balancing the iteration cost with fast asymptotic rate17:46
Recovery phase transitions18:59
Algorithm Selection map19:17
Moderate noise19:44
CGIHT recovery guarantee20:26
CGIHT projected for matrix completion21:47
NIHT, FIHT, CGIHT22:53
CGIHT: entry sensing with δ = p/mn = 1/2023:29
A few concluding observations25:27
References26:15