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From Covariance to Comode in Context of Principal Component Analysis

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Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

When it comes to the task of dimensionality reduction, the Principal Component Analysis (PCA) is among the most well known methods. Despite its popularity, PCA is prone to outliers which can be traced back to the fact that this method relies on a covariance matrix. Even with the variety of sophisticated methods to enhance the robustness of the PCA, we provide here in this work-in-progress an approach which is intriguingly simple: the covariance matrix is replaced by a so-called comode matrix. Through this minor modification the experiments show that the reconstruction loss is significantly reduced. In this work we introduce the comode and its relation to the MeanShift algorithm, including its bandwidth parameter, compare it in an experiment against the classic covariance matrix and evaluate the impact of the bandwidth hyperparameter on the reconstruction error.

inproceedings

LWDA 2019

Conference on Lernen. Wissen. Daten. Analysen. Berlin, Germany, Sep 30-Oct 02, 2019.

Authors

D. Kazempour • L. M. Yan • T. Seidl

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Research Area

 A3 | Computational Models

BibTeXKey: KYS19

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