Abstract

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 Retrievalwhere the performance can be improved by using user feedback.

Highlights

  • Many works have tried to improve the kNN algorithm by considering the geometry of the space containing examples

  • Similarity should be preferred over distance in many practical situations, e.g. text classification, information retrieval as was proved by our results on different datasets [4]

  • Two prediction rules have been developed: one is based on standard kNN while the other one considers same number of examples in different classes (SkNN-A)

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Summary

INTRODUCTION

Many works have tried to improve the kNN algorithm by considering the geometry of the space containing examples. Most of these works learn Mahanalobis distance metric, a variation of Euclidean distance. The Mahanalobis distance between two objects x and y is given by: dA(x, y) = (x − y)T M (x − y). Similarity should be preferred over distance in many practical situations, e.g. text classification, information retrieval as was proved by our results on different datasets [4]

PROBLEM FORMULATION
EXPERIMENTAL VALIDATION
SIMILARITY LEARNING AND INFORMATION RETRIEVAL
CONCLUSION
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