Data reduction for multiple functional data with class information

U. K. Jung, Myong K. Jeong, Jye Chyi Lu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The term ‘functional data’ (or ‘curve’) refers to an analog or digital signal measured during each operational cycle of a manufacturing process. Functional data contain rich information concerning the process condition and product quality for quality improvement. We propose a vertical group-wise threshold (VGWT) procedure for the reduction of multiple high-dimensional functional data containing class information. The proposed method selects important wavelet coefficients for the whole set of multiple curves by a comparison between every vertical energy metric and a threshold (VGWT). The VGWT increases the class separability with a reasonably small loss in data-reduction efficiency. A real-life example is presented to illustrate the proposed method, and a Monte Carlo simulation is performed to study the impact of different levels of class variation and noise.

Original languageEnglish (US)
Pages (from-to)2695-2710
Number of pages16
JournalInternational Journal of Production Research
Volume44
Issue number14
DOIs
StatePublished - Jul 15 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Keywords

  • Data mining
  • Data reduction
  • Functional data
  • Process control
  • Quality improvement
  • Wavelets

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