Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning

Abstract

A class of machine learning problem where each instance may either belong to one or more than one class simultaneously is known as Multi-label classification problem. Unlike other classification problems there exist several challenges and the important among them is learning the label correlation when labels are missing. In this paper, we present a unified learning system that addresses the aforementioned issue, and suggest a novel multi-label classifier termed as Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning (MLLCRS-ML). The propose classifier considers label-specific features along with utilizing the structural property of data and pairwise label correlation (both positive and negative label correlations) for recovering the missing labels. We have use hinge loss function which ensures less sensitivity towards outliers and the accelerated proximal gradient method (APG) to efficiently solve the underlying optimization problem. Experimental results on several benchmark data sets show that our propose approach MLLCRS-ML is as competitive as other state-of-the-art approaches.