Cone-restricted subspace methods

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.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Takumi Kobayashi, Nobuyuki Otsu
• Keywords: Computer Vision, Cone-restricted Subspace Methods, Feature Vectors, ICPR 2008, Ordinary Subspace Methods