Kanfer, F.H.J. (Frans)2025-01-152025-01-152025-042024-12-13*A2025http://hdl.handle.net/2263/100084Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2024.This mini-dissertation will introduce a novel mixture model of factorization machines. Factorization machines (FM) are a supervised learning class capable of learning pairwise interactions between response variables which can also be extended to interactions in higher dimensions. They are based on matrix factorization techniques attributing to their success in prediction tasks. The FM factorizes interaction terms, obtaining prediction accuracy on par with multiple linear regression (MLR). The FM also achieves this using less variables, and the model performance exceeds MLR under sparsity. Finite Gaussian mixture models (FGMM) are adept at modeling non-homogeneous populations and detecting subgroups; however, they are constructed as a combination of multiple Gaussian linear regression components. The novel model will be constructed using a combination of multiple Gaussian factorization machines to exploit the advantages of FMs when it comes to pairwise interaction terms and sparsity. The model will be estimated in an expectation-maximization (EM) algorithm setting using a coordinate descent (CD) method to estimate the FM model equation. Compared to FGMM in a sparse data setting, the novel model achieves a better fit to the data using fewer parameters and a shorter computation time.en-US© 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDSustainable Development Goals (SDGs)Mixture modelsFactorization machinesSparsityFinite mixture of factorization machinesMini Dissertation18000437https://doi.org/10.1145/2827872