Large margin pursuit for a Conic Section classifier

Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision and medical diagnosis applications. A novel Conic Section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus, directrix and eccentricity. The discriminant boundary was the locus of all points that are equi-eccentric relative to each class-representative conic section. Simpler boundaries were preferred for the sake of generalizability. In this paper, we improve the performance of the twoclass classifier via a large margin pursuit. When formulated as a non-linear optimization problem, the margin computation is demonstrated to be hard, especially due to the high dimensionality of the data. Instead, we present a geometric algorithm to compute the distance of a point to the nonlinear discriminant boundar...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Santhosh Kodipaka, Arunava Banerjee, Baba C. Vemuri
• Keywords: Class-representative Conic Section, Computer Vision, Conic Section, CVPR 2008, Discriminant Boundary, Large Margin Pursuit, Margin Computation