# Complementary to Multiple Labels: A Correlation-Aware Correction Approach

## TL;DR

This paper introduces a correlation-aware correction method for multi-labeled complementary label learning, addressing the challenge of estimating transition matrices without multi-labeled data and improving multi-label classification accuracy.

## Contribution

It proposes a novel two-step transition matrix estimation approach that incorporates label correlations, enhancing multi-label complementary label learning performance.

## Key findings

- The proposed method outperforms existing approaches in experiments.
- The correction of transition matrices improves multi-label classification accuracy.
- The approach is classifier-consistent and mitigates noise overfitting.

## Abstract

\textit{Complementary label learning} (CLL) requires annotators to give \emph{irrelevant} labels instead of relevant labels for instances. Currently, CLL has shown its promising performance on multi-class data by estimating a transition matrix. However, current multi-class CLL techniques cannot work well on multi-labeled data since they assume each instance is associated with one label while each multi-labeled instance is relevant to multiple labels. Here, we show theoretically how the estimated transition matrix in multi-class CLL could be distorted in multi-labeled cases as they ignore co-existing relevant labels. Moreover, theoretical findings reveal that calculating a transition matrix from label correlations in \textit{multi-labeled CLL} (ML-CLL) needs multi-labeled data, while this is unavailable for ML-CLL. To solve this issue, we propose a two-step method to estimate the transition matrix from candidate labels. Specifically, we first estimate an initial transition matrix by decomposing the multi-label problem into a series of binary classification problems, then the initial transition matrix is corrected by label correlations to enforce the addition of relationships among labels. We further show that the proposal is classifier-consistent, and additionally introduce an MSE-based regularizer to alleviate the tendency of BCE loss overfitting to noises. Experimental results have demonstrated the effectiveness of the proposed method.