Code Repository
Multi-Label Learning
Year: 2023
Multi-label learning with missing labels using sparse global structure for label-specific features
➡️ Description: This toolbox contains programs for the SGMML Model [1]
➡️ Requirement: The package was developed with MATLAB.
➡️ Contact: This package was developed by Mr. Sanjay Kumar. For any problem concerning the code, please feel free to contact Mr. S. Kumar.
➡️ Reference: [1] 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.
➡️ Repository Link: [Code]
Year: 2022
Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels
➡️ Description: This toolbox contains programs for the CIMML Model [1]
➡️ Requirement: The package was developed with MATLAB.
➡️ Contact: This package was developed by Mr. Sanjay Kumar. For any problem concerning the code, please feel free to contact Mr. S. Kumar.
➡️ Reference: [1] R. Rastogi and K. Sanjay, "Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels." Neural Processing Letters (2022): 1-35
➡️ Repository Link: [Code]Low rank label subspace transformation for multi-label learning with missing labels
➡️ Description: This toolbox contains programs for the LRMML Model [2]
➡️ Requirement: The package was developed with MATLAB.
➡️ Contact: This package was developed by Mr. Sanjay Kumar. For any problem concerning the code, please feel free to contact Mr. S. Kumar.
➡️ Reference: [2] K. Sanjay, and R. Rastogi. "Low rank label subspace transformation for multi-label learning with missing labels." Information Sciences 596 (2022): 53-72.
➡️ Repository Link: [Code]
Auxiliary Label Embedding for Multi-label Learning with Missing Labels
➡️ Description: This toolbox contains programs for the ALEML Model [3]
➡️ Requirement: The package was developed with MATLAB.
➡️ Contact: This package was developed by Mr. Sanjay Kumar. For any problem concerning the code, please feel free to contact Mr. S. Kumar.
➡️ Reference: [3] Kumar S., Rastogi R., "Auxiliary Label Embedding for Multi-label Learning with Missing Labels." Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. 2nd edition of Computer Vision and Machine Intelligence. (CVMI-2022)
➡️ Repository Link: [Code]
Twin-SVM
Twin Support Vector Machines for Pattern Classification
➡️ Description: This toolbox contains programs for the TSVM model [1]
➡️ Requirement: The package was developed with MATLAB.
➡️ Reference: [1] Reshma Khemchandani, Suresh Chandra (2007). Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(5), 905–910.
➡️ Repository Link: Implementation of Twin SVM [Code]; (Written by Prof. Yuan-Hai Shao for TBSVM Model)
Extreme Learning Machine
Time efficient variants of Twin Extreme Learning Machine
➡️ Description: This toolbox contains programs for the LSTELM and WLTELM models [1]
➡️ Requirement: The package was developed with MATLAB.
➡️ Contact: This package was developed by Dr. Pritam Anand. For any problem concerning the code, please feel free to contact Dr. P. Anand.
➡️ Reference: [1] Pritam Anand, Amisha Bharti, Reshma Rastogi, "Time efficient variants of Twin Extreme Learning Machine", Intelligent Systems with Applications, Volume 17, 2023, ISSN 2667-3053
➡️ Repository Link: [Code]
Miscellaneous
➡️ Some more of our project's code can be found in our Github Repository [Repository-Link]