Efficient subspace clustering of large-scale data streams with misses
Panagiotis A. Traganitis, Georgios B. Giannakis · 2016
As the amount of data generated and communicated continuously increases, clustering algorithms that are not able to handle this enormous amount of data have to be redesigned. Recent subspace clustering advances, while powerful, are computationally and memory demanding. The present paper introduces an online algorithm that broadens high-performance batch subspace clustering methods, and is able to perform subspace clustering on data arriving sequentially and possibly with misses. Numerical tests on synthetic and real data demonstrate the potential of the proposed approach.