In this letter, a new ICA algorithm, linear multilayer ICA (LMICA), is proposed. There are two phases in each layer of LMICA. One is the mapping phase, where a two-dimensional mapping is formed by moving more highly correlated (nonindependent) signals closer with the stochastic multidimensional scaling network. Another is the local-ICA phase, where each neighbor (namely, highly correlated) pair of signals in the mapping is separated by MaxKurt algorithm. Because in LMICA only a small number of highly correlated pairs have to be separated, it can extract edge detectors efficiently from natural scenes. We conducted numerical experiments and verified that LMICA generates hierarchical edge detectors from large-size natural scenes.