PT Unknown
AU Katerine Diaz
   Francesc J. Ferri
   W. Diaz
TI Fast Approximated Discriminative Common Vectors using rank-one SVD updates
BT 20th International Conference On Neural Information Processing
PY 2013
BP 368
EP 375
VL 8228
IS III
DI 10.1007/978-3-642-42051-1_46
AB An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and 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.K. Diaz-Chito,  F.J. Ferri, W. Diaz
ER