2009/04/30 by Gribonval, Remi, Schnass, Karin · 2 citations
#FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.0904.4774
This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via ℓ1-minimisation. The problem can also be seen as factorising a \ddim × \nsig matrix Y=(y1 >... y_\nsig), yn∈ \R^\ddim of training signals into a \ddim × \natoms dictionary matrix \dico and a \natoms × \nsig coefficient matrix \X=(x1... x_\nsig), xn ∈ \R^\natoms, which is sparse. The exact question studied here is when a dictionary coefficient pair (\dico,\X) can be recovered as local minimum of a (nonconvex) ℓ1-criterion with input Y=\dico \X. First, for general dictionaries and coefficient matrices, algebraic conditions ensuring local identifiability are derived, which are then specialised to the case when the dictionary is a basis. Finally, assuming a random Bernoulli-Gaussian sparse model on the coefficient matrix, it is shown that sufficiently incoherent bases are locally identifiable with high probability. The perhaps surprising result is that the typically sufficient number of training samples \nsig grows up to a logarithmic factor only linearly with the signal dimension, i.e. \nsig ≈ C \natoms log \natoms, in contrast to previous approaches requiring combinatorially many samples.