Incremental learning based on block sparse kernel nonnegative matrix factorization
Wen-Sheng Chen, Yugao Li, Binbin Pan, Bo Chen · 2016
Nonnegative matrix factorization (NMF) is a promising method for local feature extraction in face recognition. However, NMF is time-consuming when performing on a large matrix. Another limitation of NMF is that it cannot update the factors incrementally as new training data are available. To overcome these limitations, this paper proposes a block sparse kernel nonnegative matrix factorization (BSKNMF) based on the block strategy. The block trick not only reduces the computational costs, but also helps to update the factors of NMF incrementally. The kernel strategy and sparse technique are incorporated into NMF, leading to a more powerful method for feature extraction. The ORL and Yale face databases are chosen for evaluation on time efficiency and recognition rate. Compared with NMF and PNMF, the proposed approach gives the best performance.