Cone-restricted subspace methods
Takumi Kobayashi, Nobuyuki Otsu · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
In pattern recognition, feature vectors are occasionally subject to non-negative constraints. This characteristic can be expressed by a cone in feature vector space. In this paper, we propose cone-restricted subspace methods. The proposed methods admit the scaling and additivity of vectors as well as ordinary subspace methods; in addition, vectors can be strictly classified at the boundary of the cone. Some experimental results for face and person detection demonstrate the effectiveness of the proposed methods.