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Conjugate gradient iterative hard thresholding for compressed sensing and matrix completion
Published on Oct 29, 20142428 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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Chapter list
CGIHT for compressed sensing and matrix completion00:00
The simplicity of large data sets00:48
Untitled02:32
Compressed Sensing02:34
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 CS09:48
Recovery phase transitions13:14
Algorithm Selection map14:46
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