Similarity Learning in Nearest Neighbor and Application to Information Retrieval

Many people have tried to learn Mahanalobis distance metric in kNN classification by considering the geometry of the space containing examples. However, similarity may have an edge specially while dealing with text e.g. Information Retrieval. We have proposed an online algorithm, SiLA (Similarity learning algorithm) where the aim is to learn a similarity metric (e.g. cosine measure, Dice and Jaccard coefficients) and its variation eSiLA where we project the matrix learnt onto the cone of positive, semidefinite matrices. Two incremental algorithms have been developed; one based on standard kNN rule while the other one is its symmetric version. SiLA can be used in Information Retrieval where the performance can be improved by using user feedback.