2018/10/29 by Matthew Willetts, Willetts, Matthew, Aiden Doherty +5 · 1 citation
Computer Science · Mathematics · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Time Series Analysis and Forecasting #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.12176
4 pages, 2 figures, conference workshop pre-print Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
openalex publication_date 2018/10/29 · arxiv created 2018/12/11 · arxiv updated 2018/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use as many real-world datasets are like this. Here we demonstrate a new deep generative model for classification in this regime. Our model, a Gaussian mixture deep generative model, demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.