Towards the Application of Multi-view Learning in Quality of Experience Collaborative Modelling

• Published in: International Workshop on Quality of Multimedia Experience pp. 286 - 292
• Main Authors: Hamidi, MohammadAli, Porcu, Simone, Floris, Alessandro, Atzori, Luigi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.06.2024
• Subjects: Accuracy; Collaboration; Data fusion; Data integration; Data privacy; Deep learning; Estimation; Machine learning; Multi-view learning; Quality of experience; Subjective dataset; Training
• ISSN: 2472-7814
• DOI: 10.1109/QoMEX61742.2024.10598295

Multi-view (MV) learning is a machine learning technique for improving generalization efficiency by learning from different feature subsets derived from multiple sources. We believe this approach can help in Quality of Experience (QoE) modelling by integrating knowledge from different datasets generated by subjective tests conducted for the same or similar applications considering different QoE Influence Factors (IFs). To investigate this subject, in this paper, we present the experiments conducted starting from a complete dataset related to Web browsing sessions that has been artificially divided into two distinct subsets (views). The proposed MV learning approach implements a data fusion technique to integrate extracted features from different views into a unified feature space. To achieve a complete experiment on the entire problem space, all possible combinations of IFs (features) in two distinct partial views (PVs) are considered and trained in the MV approach; the full view (FV) approach, which utilizes the complete dataset, is also considered for performance comparison. Experimental results show the QoE estimation performance achieved by the MV (0.69) is comparable with that of the FV (0.72), although the 2 single views were used for training in the MV case. Moreover, the performance enhancement achieved by the MV compared with the PV is most noticeable when a lower number of features is used to train the models.