A novel multivariate analysis method with noise reduction

Shu Hao Chang*, Yu Jen Chiou, Chun Yu, Chii Wann Lin, Tzu-Chien Hsiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we develop a novel Partial Regularized Least Squares (PRLS) method which combined regularization algorithm with Partial Least Squares (PLS) analysis for noise reduction application. In general, Least Squares and PLS fall into an overfitting problem with ill-posed condition. It means that some feature selections make the training data to have better adaptability to the model, but the quality of prediction would be poorly compared to the training data for the testing information. We usually expected that the selected model should have consistent predicted result between the training data and testing data. In order to evaluate the performance of PRLS method, we generate two simulation data, i.e. cosine waveform and 8th polynomial waveform with Gaussian distribution noisy for calculating two values, i.e. Correlation Coefficient value (RR value) and Root Mean Square Error (RMSE) for testing. The results show that the RR value of PRLS is higher than PLS's at increasing noise-to-signal ratio. As well the RMSE of PRLS is lower than PLS's at same S/N ratio. We also show that the PRLS approximates to the desired output at calibration. It can be applied in real-world noise reduction in the future.

Original languageEnglish
Title of host publication4th European Conference of the International Federation for Medical and Biological Engineering - ECIFMBE 2008
Pages133-137
Number of pages5
DOIs
StatePublished - 1 Dec 2008
Event4th European Conference of the International Federation for Medical and Biological Engineering, ECIFMBE 2008 - Antwerp, Belgium
Duration: 23 Nov 200827 Nov 2008

Publication series

NameIFMBE Proceedings
Volume22
ISSN (Print)1680-0737

Conference

Conference4th European Conference of the International Federation for Medical and Biological Engineering, ECIFMBE 2008
CountryBelgium
CityAntwerp
Period23/11/0827/11/08

Keywords

  • Multivariate Analysis
  • Noise Reduction
  • Partial Least Squares
  • Regularization

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