  This letter proposes a dictionary learning algorithm for blind one bit
compressed sensing. In the blind one bit compressed sensing framework, the
original signal to be reconstructed from one bit linear random measurements is
sparse in an unknown domain. In this context, the multiplication of measurement
matrix $\Ab$ and sparse domain matrix $\Phi$, \ie $\Db=\Ab\Phi$, should be
learned. Hence, we use dictionary learning to train this matrix. Towards that
end, an appropriate continuous convex cost function is suggested for one bit
compressed sensing and a simple steepest-descent method is exploited to learn
the rows of the matrix $\Db$. Experimental results show the effectiveness of
the proposed algorithm against the case of no dictionary learning, specially
with increasing the number of training signals and the number of sign
measurements.
