Fast Approximated Discriminative Common Vectors Using Rank-One SVD Updates

An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reductionand classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.