4 papers
Favourable Missingness in Semi-Supervised Classification for Exponential Mixture Models
Huanchao Zhou, Jinran Wu, Fariborz Setoudehtazang +1
Semi-supervised classifiers are commonly trained from samples in which all features are observed but some class labels are missing. When label missingness is independent of the obs…
Robust Deep Mixture Models
Jinran Wu, Geoffrey J. McLachlan
We propose a robust deep mixture model based on a pathway-wise shared scale-mixture construction. Layer-specific component indicators are independently distributed according to the…
SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness
Geoffrey J. McLachlan, Jinran Wu
Partially labelled samples arise when features are observed for all data, but class labels are available for only a subset. In such settings, the mechanism governing label availabi…
Informative missingness and its implications in semi-supervised learning
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
Semi-supervised learning (SSL) constructs classifiers using both labelled and unlabelled data. It leverages information from labelled samples, whose acquisition is often costly or…