Incremental kernel non-negative matrix factorization for hyperspectral unmixing

In this paper, we proposed an incremental kernel non-negative matrix factorization (IKNMF) to reduce the computing scale in hyperspectral unmixing. Kernel non-negative matrix factorization (KNMF) is an extended non-negative matrix factorization (NMF) able to capture nonlinear dependency features in data matrix through kernel functions. In KNMF algorithm, the size of kernel matrices is closely associated with the input data scale. To reduce calculation and storage of large matrices, we extend KNMF by introducing partition matrix theory. The decomposition results of data matrices are derived from smaller scale matrices incrementally. Experiments are conducted on synthetic hyperspectral images with multiple sizes, and the experimental results show that the proposed algorithm have effect in saving calculation and memory resource without degrading the unmixing performance.