Version 1
: Received: 1 November 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (12:06:26 CET)
How to cite:
Nguyen, L. Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model. Preprints2020, 2020110038. https://doi.org/10.20944/preprints202011.0038.v1
Nguyen, L. Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model. Preprints 2020, 2020110038. https://doi.org/10.20944/preprints202011.0038.v1
Nguyen, L. Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model. Preprints2020, 2020110038. https://doi.org/10.20944/preprints202011.0038.v1
APA Style
Nguyen, L. (2020). Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model. Preprints. https://doi.org/10.20944/preprints202011.0038.v1
Chicago/Turabian Style
Nguyen, L. 2020 "Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model" Preprints. https://doi.org/10.20944/preprints202011.0038.v1
Abstract
Dyadic data which is also called co-occurrence data (COD) contains co-occurrences of objects. Searching for statistical models to represent dyadic data is necessary. Fortunately, finite mixture model is a solid statistical model to learn and make inference on dyadic data because mixture model is built smoothly and reliably by expectation maximization (EM) algorithm which is suitable to inherent spareness of dyadic data. This research summarizes mixture models for dyadic data. When each co-occurrence in dyadic data is associated with a value, there are many unaccomplished values because a lot of co-occurrences are inexistent. In this research, these unaccomplished values are estimated as mean (expectation) of random variable given partial probabilistic distributions inside dyadic mixture model.
Keywords
dyadic data; co-occurrence data; expectation maximization (EM) algorithm; mixture model
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
