Incremental Learning of Auto-Association Multilayer Perceptrons Network

This paper introduces a new algorithm to reduce the time of updating the weights of auto-association multilayer perceptrons network. The basic idea is to modify the singular value decomposition which has been used in the batch algorithm to update the weights whenever a new row is added to the input matrix. The computation analysis and the experiments show that the new algorithm speeds up the implementation about 5-8 times.