Large-Scale Local Online Similarity/Distance Learning Framework Based on Passive/Aggressive

Abstract

Similarity/distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of the metric learning field. Many metric learning algorithms learn a global distance function from data that satisfies the constraints of the problem. However, in many real-world datasets, where the discrimination power of features varies in the different regions of input space, a global metric is often unable to capture the complexity of the task. To address this challenge, local metric learning methods are proposed which learn multiple metrics across the different regions of the input space. Some advantages of these methods include high flexibility and learning a nonlinear mapping, but they typically achieve at the expense of higher time requirements and overfitting problems. To overcome these challenges, this research presents an online multiple metric learning framework. Each metric in the proposed framework is composed of a global and a local component learned simultaneously. Adding a global component to a local metric efficiently reduces the problem of overfitting. The proposed framework is also scalable with both sample size and the dimension of input data. To the best of our knowledge, this is the first local online similarity/distance learning framework based on Passive/Aggressive (PA). In addition, for scalability with the dimension of input data, Dual Random Projection (DRP) is extended for local online learning in the present work. It enables our methods to run efficiently on high-dimensional datasets while maintaining their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA. Experimental results on some challenging datasets from machine vision community confirm that the extended methods considerably enhance the performance of the related global ones without increasing the time complexity.