PT Journal
AU Katerine Diaz
   Jesus Martinez del Rincon
   Aura Hernandez-Sabate
TI Decremental generalized discriminative common vectors applied to images classification
SO Knowledge-Based Systems
JI KBS
PY 2017
BP 46
EP 57
VL 131
DE Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification
AB In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.
ER