On reducing feature dimensionality for partial discharge diagnosis applications

Abstract

Feature dimensionality reduction is a critical task in various machine learning applications including prognostics and health management (PHM) applications. Linear transformations, most popularly principal component analysis (PCA) and linear discriminant analysis (LDA), are the most widely-used methods for feature dimensionality reduction. For classification problems, LDA, being a supervised linear transformation that aims at maximally retaining class discriminant information, is generally considered to be a better method than PCA, an unsupervised method. However, LDA suffers from the singularity or small sample size problem. Attempting to address this problem, in this paper we propose a cluster-based LDA (cLDA) for feature dimensionality reduction. It first partitions features in distinct clusters and then performs cluster-wise LDA transformation. We demonstrate the effectiveness of the proposed cLDA on reducing the number of features by using a real-world PHM application - partial discharge diagnosis.