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.

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.

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.

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.

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.

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