Transductive Multilabel Learning via Label Set Propagation

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Transductive Multi-View Zero-Shot Learning (2015, methodology): “[17] studied transductive multi-label learning with a small set of training instances.”
Visual Classification by ℓ1-Hypergraph Modeling (2015, background): “In previous work, there are two types of strategies for building a hypergraph, i.e., attribute-based and neighborhood-based approaches.”
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning (2013, result): “This is in contrast to many studies on common semi-supervised or transductive multi-label learning [18, 24, 26, 43] where each labeled instance receives a complete set of label assignments.”
The Emerging Trends of Multi-Label Learning (2020, background): “In fact, similar to MLML, a plenty of SS-MLC algorithms are also based on graph models [93], [94], and low-rank assumptions [79], [95].”
A Review on Multi-Label Learning Algorithms (2014, background): “that two examples with high similarity in the input space tend to have large overlap in the output space [20], [86], [95], [165], [168], [169], [184], [185].”
Multi-Label Learning with Global and Local Label Correlation (2017, unknown): “These datasets have been widely used in multi-label learning literatures [7], [8], [11], [14], [15], [22], [26], [36], [37], [38], [39], [40], [41], [42] etc.”