  In this study, a spectral graph-theoretic grouping strategy for weakly
supervised classification is introduced, where a limited number of labelled
samples and a larger set of unlabelled samples are used to construct a larger
annotated training set composed of strongly labelled and weakly labelled
samples. The inherent relationship between the set of strongly labelled samples
and the set of unlabelled samples is established via spectral grouping, with
the unlabelled samples subsequently weakly annotated based on the strongly
labelled samples within the associated spectral groups. A number of similarity
graph models for spectral grouping, including two new similarity graph models
introduced in this study, are explored to investigate their performance in the
context of weakly supervised classification in handling different types of
data. Experimental results using benchmark datasets as well as real EMG
datasets demonstrate that the proposed approach to weakly supervised
classification can provide noticeable improvements in classification
performance, and that the proposed similarity graph models can lead to ultimate
learning results that are either better than or on a par with existing
similarity graph models in the context of spectral grouping for weakly
supervised classification.
