A few questions about KSVD algorithm (dictionary learning) in a paper

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:

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!

• 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 May 17 '15 at 6:32

• $x_{T}^{k}$ denotes the $k$th row in $X$
• $K-SVD$ 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):