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

TL;DR

This paper introduces a scalable, online local similarity/distance learning framework based on Passive/Aggressive algorithms, combining global and local components to improve flexibility and reduce overfitting in high-dimensional data.

Contribution

It presents the first local online similarity/distance learning framework based on PA, incorporating dual random projection for high-dimensional scalability.

Findings

Framework is scalable with sample size and data dimension.

Combines global and local metrics to reduce overfitting.

Maintains high predictive performance on high-dimensional datasets.

Abstract

Similarity/Distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of metric learning field. Many metric learning algorithms learn a global distance function from data that satisfy the constraints of the problem. However, in many real-world datasets that 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 that learn multiple metrics across the different regions of input space. Some advantages of these methods are high flexibility and the ability to learn a nonlinear mapping but typically achieves at the expense of higher time requirement and overfitting problem. 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 reduce 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 PA (Passive/Aggressive). In addition, for scalability with the dimension of input data, DRP (Dual Random Projection) is extended for local online learning in the present work. It enables our methods to be run efficiently on high-dimensional datasets, while maintains their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA.