A Fast Logdet Divergence Based Metric Learning Algorithm for Large Data Sets Classification

Jiangyuan Mei,Jian Hou,Jicheng Chen,Hamid Reza Karimi

doi:10.1155/2014/463981

Abstract

Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis matrix obtained this way measures the distances between instances accurately and serves as the basis of classifiers, for example, thek-nearest neighbors classifier. Experiments on benchmark data sets demonstrate that the proposed algorithm compares favorably with the state-of-the-art methods.

Highlights

Large data sets classification has become one of the hottest research topics since it is the building block in many industrial and computer vision applications, such as fault diagnosis in complicated systems [1, 2], automated optical inspection for complex workpieces [3], and face recognition in large-capacity databases [4]
In dealing with the challenge from numerous instances, we find online metric learning as a good solution
To address the challenges and opportunities raised by larger data sets, this paper proposes a new metric learning strategy

Summary

Introduction

Large data sets classification has become one of the hottest research topics since it is the building block in many industrial and computer vision applications, such as fault diagnosis in complicated systems [1, 2], automated optical inspection for complex workpieces [3], and face recognition in large-capacity databases [4]. The second one is to assign weights Σ to the new feature These two functions enable Mahalanobis distance to measure the distance between instances effectively. In a process control system [9,10,11], various sensors are utilized to collect a group of feature data at one time, which may influence the Mahalanobis distance used in detecting fault. In this situation, online metric learning can be used to address the need of Mahalanobis distance updating.

Online Logdet Divergence Based Metric Learning Model

Compressed Representation for High Dimensional Mahalanobis Matrix

Experiments Results

Conclusion