Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model

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. Preprints 2020, 2020110038 (doi: 10.20944/preprints202011.0038.v1). Nguyen, L. Learning Dyadic Data and Predicting Unaccomplished Co-Occurrent Values by Mixture Model. Preprints 2020, 2020110038 (doi: 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.

Subject Areas

dyadic data; co-occurrence data; expectation maximization (EM) algorithm; mixture model

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