Robust Sparse Component Analysis Based on a Generalized Hough Transform. (2nd October 2006) - Theis Theis, Fabian J. Fabian J.; Georgiev Georgiev, Pando Pando; Cichocki Cichocki, Andrzej Andrzej
Abstract : An algorithm called Hough SCA is presented for recovering the matrixA inx ( t ) = A s ( t ), wherex ( t ) is a multivariate observed signal, possibly is of lower dimension than the unknown sourcess ( t ) . They are assumed to be sparse in the sense that at every time instantt, s ( t ) has fewer nonzero elements than the dimension ofx ( t ) . The presented algorithm performs a global search for hyperplane clusters within the mixture space by gathering possible hyperplane parameters within a Hough accumulator tensor. This renders the algorithm immune to the many local minima typically exhibited by the corresponding cost function. In contrast to previous approaches, Hough SCA is linear in the sample number and independent of the source dimension as well as robust against noise and outliers. Experiments demonstrate the flexibility of the proposed algorithm.