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To learn more about dictionary learning, I am currently trying to understand the concept in detail and to do so, I've found the following paper quite informative:

KSVD: an algorithm for designing overcomplete dictionaries for sparse representation

UPDATE1 (a short explanation of the whole concept):

For most signals, there exists a sparse representation, considering known bases. The bases we use to represent data forms a matrix, called dictionary. In the linear equation: $Y=DX$, $Y$ is our data/signals; $D$ is the dictionary; $X$ is the sparse representation of signals using the dictionary


I have a few questions:

  • does the jth column of D (the overcomplete dictionary) correspond to the jth row of X or its transpose? So we do work on the transpose of X and not the matrix X itself? Why the notation $x_T ^K$ is used and not $x_K ^T$ ?
  • somewhere in the middle, they suggest to initialize $w_k$ which defines as follows: $$w_k=\{i|1 \leq k \leq K: w_k(i) \neq 0\}$$ is defining this variable necessary in spite of the fact that we will define another variable afterwards. $\Omega _k$ is a matrix of size $N$ (number of signals (Y)) $\times |w_k|$ where it is one for $w_k(i)$ and zero otherwise. why do we need to define $w_k$?

  • According to the authors we have the following matrix for error matrix:

$$E_k=Y-\sum_{k \neq j} d_jx_T^{j}$$

(Y is an $n \times N$ matrix (signals), D is a $n \times K$ matrix (the overcomplete dictionary) and $X$ is $K \times N$ (the sparse representation of signals using the dictionary D)

I am not sure about the summation, how is it computed? multiplying each column of D by the corresponding row in X? Please shed some light on the problem!

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  • $\begingroup$ I need 0the answer to these questions to continue my research but after a few days, didn't get answer/comment: cross-posted on cs theory $\endgroup$
    – Gigili
    May 17 '15 at 6:32
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  • $x_{T}^{k}$ denotes the $k$th row in $X$
  • The main idea is that: the authors first defined some dictionary, and then based on them, using K-SVD to denoising the image. After that, the dictionary is updated by OMP (Orthogonal Matching Pursuit). This work will be iteratively done until satisfying convergence condition.
  • $K-SVD$ is a very original paper on dictionary learning, the authors are initially defined $w_{k}$. But, the following work from other researchers show that it is not quite good. Tons of papers have been trying to improve the performance of $K-SVD$. For better performance, you may read some papers which don't need to initially define the dictionary (dictionary is defined by noisy data):

[1] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3D Transform-domain Collaborative Filtering,” IEEE Trans. on Image Process., vol. 16, no. 8, pp. 2080-2095, Aug. 2007

[2] K. Dabov, A. Foi, V. Katkvnik, K. Egiazarian, “BM3D Image Denoising with Shape-Adaptive Principal Component Analysis,” Proc. of Workshop on Signal Process. With Adaptive Sparse Structure Representations, 2009

[3] P. Chatterjee, and P. Milanfar, “Patch-Based Near-Optimal Image Denoising,” IEEE Trans. on Image Process., vol. 21, no. 4, pp. 1635-1649, Apr. 2012

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  • $\begingroup$ There exist dictionary methods of the last 2-3 years which surpass BM3D, given that they are allowed to train on very many images. For instance J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, \Non-local sparse models for image restoration," in Computer Vision, 2009 IEEE 12th International Conference on , pp. 2272{2279, Sept 2009. The question is usually: for our application what are reasonable assumptions for our data? Even though performance may be higher for some methods, if the assumptions are not valid or acceptable, they should not be used. $\endgroup$ May 25 '15 at 11:45
  • $\begingroup$ Sorry for the crappy layout, but this was all that I could manage to get into a comment and my contribution wasn't really good enough to be an answer. $\endgroup$ May 25 '15 at 11:46

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