We propose the kernel-based nonlinear independent component analysis (ICA) method  which consists of two separate steps First  we map the data to a high-dimensional feature space and perform dimension reduction to extract the effective subspace  which was achieved by kernel principal component analysis (PCA) and can be considered as a pre-processing step