@inproceedings{6fe67972aa9a4d369d6e6246aa769e94,
title = "Subspace Support Vector Data Description",
abstract = "This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.",
keywords = "image classification, image representation, iterative methods, optimisation, support vector machines, subspace support vector data description, linear mappings, low-dimensional feature space, optimal hypersphere enclosing, compact class representation, one-class classification methods, data mapping, one-class classification problems, Optimization, Training, Kernel, Data models, Training data, Support vector machine classification, One-class Classification, Support Vector Data Description, Subspace Learning",
author = "Fahad Sohrab and Jenni Raitoharju and Moncef Gabbouj and Alexandros Iosifidis",
note = "EXT={"}Iosifidis, Alexandros{"}; International Conference on Pattern Recognition ; Conference date: 01-01-1900",
year = "2018",
month = aug,
doi = "10.1109/ICPR.2018.8545819",
language = "English",
isbn = "978-1-5386-3789-0",
publisher = "IEEE",
pages = "722--727",
booktitle = "2018 24th International Conference on Pattern Recognition (ICPR)",
address = "United States",
}