Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance

TL;DR

This paper introduces a novel approach to label distribution learning that accounts for hidden labels, utilizing proportional information, feature similarity, and low-rank structures, with theoretical recovery guarantees and superior experimental results.

Contribution

It proposes a new HidLDL framework that effectively recovers complete label distributions from incomplete data with hidden labels, improving over existing methods.

Findings

Our method outperforms state-of-the-art LDL and IncomLDL techniques.

Theoretical recovery bounds validate the approach.

Experimental results demonstrate improved label distribution recovery and prediction accuracy.

Abstract

Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, which leads to the birth of incomplete label distribution learning (IncomLDL). All the previous IncomLDL methods set the description degrees of "missing" labels in an instance to 0, but remains those of other labels unchanged. This setting is unrealistic because when certain labels are missing, the degrees of the remaining labels will increase accordingly. We fix this unrealistic setting in IncomLDL and raise a new problem: LDL with hidden labels (HidLDL), which aims to recover a complete label distribution from a real-world incomplete label distribution where certain labels in an instance are omitted during annotation. To solve this challenging problem, we discover the significance of proportional information of the observed labels and capture it by an innovative constraint to utilize it during the optimization process. We simultaneously use local feature similarity and the global low-rank structure to reveal the mysterious veil of hidden labels. Moreover, we theoretically give the recovery bound of our method, proving the feasibility of our method in learning from hidden labels. Extensive recovery and predictive experiments on various datasets prove the superiority of our method to state-of-the-art LDL and IncomLDL methods.