Abstract

Data correlation is an old great problem in multivariate analysis. In this paper a new correlation index, called K, is proposed to evaluate the correlation content into the data. Their mathematical properties are simple and their behavior is tested on some theoretical cases and compared with other correlation indices on 31 real data sets. From the proposed K correlation index, two functions are derived with the aim to estimate the significant number of principal components to retain in Principal Component Analysis. An extensive comparison with several other methods is also performed on real data sets. The obtained results show that the two functions give a number of significant principal components which can be interpreted as the maximum theoretical number and the safest number, respectively.

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 call

Disclaimer: 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.