Supporting Data and Code: Fast clustering in linear independent 1D subspaces: segmentation of multi-channel images with high spatial resolution

Algorithms for subspace clustering (SC) such as sparse and low- rank representation SC are effective in terms of the accuracy but suffer from high computational complexity. We propose algorithm for SC of (highly) similar data points drawn from union of linear independent one-dimensional subspaces with computational complexity that is linear in number of data points. The algorithm finds a dictionary that represents data in reproducible kernel Hilbert space (RKHS). Afterwards, data are projected into RKHS by using empirical kernel map (EKM). Segmentation into subspaces is realized by applying the max operator on projected data. We provide rigorous proof that for noise free data proposed approach yields exact clustering into subspaces. We also prove that EKM-based projection yields less correlated data points. Due to nonlinear projection, the proposed method can adopt to linearly nonseparable data points. We demonstrate accuracy and computational efficiency of the proposed algorithm on synthetic dataset as well as on segmentation of tissue components from image of unstained specimen in histopathology