@inproceedings{bodesheim2013kernel,
    type = {inproceedings},
    key = {bodesheim2013kernel},
    title = {Kernel Null Space Methods for Novelty Detection},
    author = {Paul Bodesheim and Alexander Freytag and Erik Rodner and Michael Kemmler and Joachim Denzler},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2013},
    pages = {3374-3381},
    abstract = {Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and ImageNet. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.},
    code = {https://github.com/cvjena/knfst},
    doi = {10.1109/CVPR.2013.433},
    groups = {lifelonglearning,noveltydetection},
    url = {https://dx.doi.org/10.1109/CVPR.2013.433},
}