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@ml-lab-sau

Machine Learning & Statistical Inference Lab

The "MLSI-LAB" serves as a dedicated research facility specializing in Machine Learning, led by Dr. Reshma Rastogi at South Asian University.

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  1. Low-rank-label-subspace-transformation-for-multi-label-learning-with-missing-labels Low-rank-label-subspace-transformation-for-multi-label-learning-with-missing-labels Public

    The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label …

    MATLAB 4

  2. Time-efficient-variants-of-Twin-Extreme-Learning-Machine Time-efficient-variants-of-Twin-Extreme-Learning-Machine Public

    In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifi…

    MATLAB

Repositories

Showing 9 of 9 repositories
  • BT-MA Public

    BT-MA a Machine learning model

    ml-lab-sau/BT-MA’s past year of commit activity
    0 MIT 0 0 0 Updated Jul 12, 2024
  • UniTSVM Public
    ml-lab-sau/UniTSVM’s past year of commit activity
    0 0 0 0 Updated May 27, 2024
  • Time-efficient-variants-of-Twin-Extreme-Learning-Machine Public

    In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense.

    ml-lab-sau/Time-efficient-variants-of-Twin-Extreme-Learning-Machine’s past year of commit activity
    MATLAB 0 0 0 0 Updated Jan 28, 2024
  • Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features Public

    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.

    ml-lab-sau/Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features’s past year of commit activity
    MATLAB 0 1 0 0 Updated Jan 28, 2024
  • Discriminatory-Label-specific-Weights-for-Multi-label-Learning-with-Missing-Labels Public

    To deal with the class imbalance problem in multi-label learning with missing labels, we propose Class Imbalance aware Missing labels Multi-label Learning, CIMML. Our proposed method handles class imbalance issue by constructing a label weight matrix with weight estimation guided by how frequently a label is present, absent, and unobserved.

    ml-lab-sau/Discriminatory-Label-specific-Weights-for-Multi-label-Learning-with-Missing-Labels’s past year of commit activity
    MATLAB 0 0 0 0 Updated Jan 28, 2024
  • Low-rank-label-subspace-transformation-for-multi-label-learning-with-missing-labels Public

    The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label matrix which facilitates the missing label information recovery.

    ml-lab-sau/Low-rank-label-subspace-transformation-for-multi-label-learning-with-missing-labels’s past year of commit activity
    MATLAB 4 0 0 0 Updated Jan 28, 2024
  • Auxiliary-Label-Embedding-for-Multi-label-Learning-with-Missing-Labels Public

    In this paper, we propose an approach for multi-label classification when label details are incomplete by learning auxiliary label matrix from the observed labels, and generating an embedding from learnt label correlations preserving the correlation structure in model coefficients.

    ml-lab-sau/Auxiliary-Label-Embedding-for-Multi-label-Learning-with-Missing-Labels’s past year of commit activity
    MATLAB 0 0 0 0 Updated Jan 28, 2024
  • Neo-Twin-Support-Vector-Machine Public

    In this paper, a new variant of Twin Support Vector Machines (TSVM) termed as Neo-Twin Support Vector Machines (Neo-TSVM) has been proposed for binary pattern classification. AUTHORS: Sambhav Jain; Shuvo Saha Roy; Reshma Rastogi

    ml-lab-sau/Neo-Twin-Support-Vector-Machine’s past year of commit activity
    MATLAB 1 0 0 0 Updated Feb 20, 2023
  • MLC_toolbox Public Forked from KKimura360/MLC_toolbox

    A MATLAB/OCTAVE library for Multi-Label Classification

    ml-lab-sau/MLC_toolbox’s past year of commit activity
    MATLAB 0 GPL-3.0 23 0 0 Updated Dec 4, 2018

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