An Invariant Large Margin Nearest Neighbour Classifier

The k-nearest neighbour (kNN) rule is a simple and effective method for multi-way classification that is much used in Computer Vision. However, its performance depends heavily on the distance metric being employed. The recently proposed large margin nearest neighbour (LMNN) classifier [21] learns a distance metric for kNN classification and thereby improves its accuracy. Learning involves optimizing a convex problem using semidefinite programming (SDP). We extend the LMNN framework to incorporate knowledge about invariance of the data. The main contributions of our work are three fold: (i) Invariances to multivariate polynomial transformations are incorporated without explicitly adding more training data during learning - these can approximate common transformations such as rotations and affinities; (ii) the incorporation of different regularizers on the parameters being learnt; and (iii) for all these variations, we show that the distance metric can still be obtained by solving a con...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
• Keywords: Computer Vision, Convex Sdp Problem, Distance Metric, ICCV 2007, K-nearest Neighbour, KNN Classification, Standard Iris Dataset