Graph-based classification for multiple observations of transformed patterns

We consider the problem of classification when multiple observations of a pattern are available, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all the unlabelled samples belong to the same unknown class. We build on graph-based methods for semisupervised learning and we optimize the graph construction in order to exploit the special structure of the problem. In particular, we assume that the optimal adjacency matrix is a linear combination of all possible class-conditional ideal adjacency matrices. We formulate the construction of the optimal adjacency matrix as a linear program (LP) on the weights of the linear combination. We provide experimental results that show the effectiveness and the validity of the proposed methodology.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Effrosini Kokiopoulou, Pascal Frossard, Stefanos Pirillos
• Keywords: Computer Vision, ICPR 2008, Ideal Adjacency Matrices, Linear Combination, Optimal Adjacency Matrix