Abstract
AbstractA new outlier detection technique has been created which functions effectively with non‐bilinear data, a situation in which more common detection techniques have difficulty. The method involves the calculation of the data set's fractal dimension in the latent variable score space. For batch data sets a full cross‐validation is used to find outliers, while process data are analyzed using a training set. If a spectrum in either case significantly changes the fractal dimension statistically, then it is identified as an outlier. The technique shows good results for surface plasmon resonance data sets and has been shown to be effective in detecting even subtle outliers. Copyright © 2004 John Wiley & Sons, Ltd.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
