Showing posts with label dictionary learning. Show all posts
Showing posts with label dictionary learning. Show all posts
Friday, April 8, 2011
Tim Machado : April 12
Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis by Youngmin Cho and Lawrence K. Saul
Thursday, March 3, 2011
Adaptive Compressive Sensing
Fritz Sommer gave a COSYNE 2011 workshop presentation of seemingly magical results coauthored by Guy Isely and Christopher Hillar.
Suppose an area of the brain deals in a signal which is sparse in some underlying unknown dictionary. This area subsamples the signal with say a random measurement matrix, and sends the subsampled signal to another area. The receiving area doesn't know what the original signals were, or what the underlying sparsifying dictionary was, or what the measurement matrix were; all it knows are the subsampled measurements it has received. If the receiving area learns a dictionary in which the subsampled signals it received are sparse, can this sparse representation also be used to linearly represent the original signal? The answer is yes.
To restore normality and disprove magic, read their NIPS paper. Apparently a longer paper with proofs is due to come out soon.
Suppose an area of the brain deals in a signal which is sparse in some underlying unknown dictionary. This area subsamples the signal with say a random measurement matrix, and sends the subsampled signal to another area. The receiving area doesn't know what the original signals were, or what the underlying sparsifying dictionary was, or what the measurement matrix were; all it knows are the subsampled measurements it has received. If the receiving area learns a dictionary in which the subsampled signals it received are sparse, can this sparse representation also be used to linearly represent the original signal? The answer is yes.
To restore normality and disprove magic, read their NIPS paper. Apparently a longer paper with proofs is due to come out soon.
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