Date of Award
1-1-2010
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science
First Advisor
Chris Gauthier-Dickey, Ph.D.
Second Advisor
Ramki Thurimella, Ph.D.
Third Advisor
Peter Laz
Keywords
Collaborative filtering, Correlation coefficient, Nearest neighbor, Pearson, Reduce error
Abstract
Pearson product-moment correlation coefficients are a well-practiced quantification of linear dependence seen across many fields. When calculating a sample-based correlation coefficient, the accuracy of the estimation is dependent on the quality and quantity of the sample. Like all statistical models, these correlation coefficients can suffer from overfitting, which results in the representation of random error instead of an underlying trend.
In this paper, we discuss how Pearson's product-moment correlation coefficients can utilize information outside of the two items for which the correlation is being computed. By introducing a relationship with one or more additional items that meet specified criterion, our Transitive Pearson product-moment correlation coefficient can significantly reduce the error, up to over 50%, of sparse, sample-based estimations.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Taylor Phillips
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
35 p.
Recommended Citation
Phillips, Taylor, "Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients" (2010). Electronic Theses and Dissertations. 515.
https://digitalcommons.du.edu/etd/515
Copyright date
2010
Discipline
Computer science