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Author (up) Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate
Title Decremental generalized discriminative common vectors applied to images classification Type Journal Article
Year 2017 Publication Knowledge-Based Systems Abbreviated Journal KBS
Volume 131 Issue Pages 46-57
Keywords Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; 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.
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Notes ADAS; 600.118; 600.121 Approved no
Call Number Admin @ si @ DMH2017a Serial 3003
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