Semi-unsupervised Learning of Human Activity using Deep Generative Models

Autor:	Willetts, Matthew, Doherty, Aiden, Roberts, Stephen, Holmes, Chris
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, 2 figures, conference workshop pre-print Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.12176 Zobrazit plný text záznamu View this record from Arxiv