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dc.contributor.authorChang, Shu-Haoen_US
dc.contributor.authorChiou, Yu-Jenen_US
dc.contributor.authorYu, Chunen_US
dc.contributor.authorLin, Chii-Wannen_US
dc.contributor.authorHsiao, Tzu-Chienen_US
dc.date.accessioned2014-12-08T15:22:14Z-
dc.date.available2014-12-08T15:22:14Z-
dc.date.issued2009en_US
dc.identifier.isbn978-3-540-89207-6en_US
dc.identifier.issn1680-0737en_US
dc.identifier.urihttp://hdl.handle.net/11536/15749-
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.subjectMultivariate Analysisen_US
dc.subjectPartial Least Squaresen_US
dc.subjectRegularizationen_US
dc.subjectNoise Reductionen_US
dc.titleA Novel Multivariate Analysis Method with Noise Reductionen_US
dc.typeProceedings Paperen_US
dc.identifier.journal4TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERINGen_US
dc.citation.volume22en_US
dc.citation.issue1-3en_US
dc.citation.spage133en_US
dc.citation.epage137en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000299998500034-
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