@Article{KaterineDiaz2017,
author="Katerine Diaz
and Jesus Martinez del Rincon
and Aura Hernandez-Sabate",
title="Decremental generalized discriminative common vectors applied to images classification",
journal="Knowledge-Based Systems",
year="2017",
volume="131",
pages="46--57",
optkeywords="Decremental learning",
optkeywords="Generalized Discriminative Common Vectors",
optkeywords="Feature extraction",
optkeywords="Linear subspace methods",
optkeywords="Classification",
abstract="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.",
optnote="ADAS; 600.118; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3003), last updated on Wed, 26 Jan 2022 09:08:45 +0100",
opturl="https://doi.org/10.1016/j.knosys.2017.05.020",
file=":http://refbase.cvc.uab.es/files/DMH2017a.pdf:PDF"
}