TY  - JOUR
AU  - Katerine Diaz
AU  - Jesus Martinez del Rincon
AU  - Aura Hernandez-Sabate
PY  - 2017//
TI  - Decremental generalized discriminative common vectors applied to images classification
T2  - KBS
JO  - Knowledge-Based Systems
SP  - 46
EP  - 57
VL  - 131
KW  - Decremental learning
KW  - Generalized Discriminative Common Vectors
KW  - Feature extraction
KW  - Linear subspace methods
KW  - Classification
N2  - 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.
UR  - https://doi.org/10.1016/j.knosys.2017.05.020
L1  - http://refbase.cvc.uab.es/files/DMH2017a.pdf
N1  - ADAS; 600.118; 600.121
ID  - Katerine Diaz2017
ER  -