Kernel-based low-rank feature extraction on a budget for big data streams

With nowadays big data torrent, identifying low-dimensional latent structures and extracting features from massive datasets are tasks of paramount importance. To this end, as real data generally lie on (or close to) nonlinear manifolds, kernel-based approaches are well motivated. Being nonparametric, unfortunately kernel-based feature extraction incurs complexity that grows prohibitively with the number of data. In response to this formidable challenge, the present work puts forward a low-rank, kernel-based feature extraction method, where the number of kernel functions is confined to an affordable budget. The resultant algorithm is particularly tailored for online operation, where data streams need not even be stored in memory. Tests on synthetic and real datasets demonstrate and benchmark the efficiency of the proposed method on linear classification applied to the extracted features.