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

A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping