➡️ Implementation of SGMML Model
➡️ If you use SGMML's Model and functionality in a scientific publication, please cite the following paper:
K. Sanjay, N. Ahmadi, and R. Rastogi, "Multi-label learning with missing labels using sparse global structure for label-specific features." Applied Intelligence (2023): 1-16.
BibTeX entry:
@article{10.1007/s10489-022-04439-7,
author = {Kumar, Sanjay and Ahmadi, Nadira and Rastogi, Reshma},
title = {Multi-label learning with missing labels using sparse global structure for label-specific features},
year = {2023},
issue_date = {Aug 2023},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {53},
number = {15},
issn = {0924-669X},
url = {https://doi.org/10.1007/s10489-022-04439-7},
doi = {10.1007/s10489-022-04439-7},
abstract = {Multi-label learning associates a given data instance with one or several class labels. A frequent problem with real life multi-label datasets is the lack of complete label information. Incomplete labels increase model complexity as the label correlation information is not reliable, resulting in a suboptimal multi-label classifier. Further, high dimensionality of multi-label datasets often introduces spurious feature-label dependencies. Thus, discovering label-specific features is imperative for efficient handling of high-dimensional data for multi-label learning with missing labels. To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data. Instances are expressed as linear combination of label-specific features and the inter-relation guides the construction of model coefficients. The model also constructs supplementary label correlations to assist missing label recovery as part of the optimization problem. Empirical results on benchmark multi-label datasets highlight the effectiveness of the proposed method.},
journal = {Applied Intelligence},
month = {jan},
pages = {18155–18170},
numpages = {16},
keywords = {Label-specific features, Auxiliary label correlations, Sparse global structure, Missing labels, Multi-label learning}
}