Label Distribution Learning by Exploiting Fuzzy Label Correlation

Researchers have proposed to exploit label correlation to alleviate the exponential-size output space of label distribution learning (LDL). In particular, some have designed LDL methods to consider local label correlation. These methods roughly partition the training set into clusters and then exploit local label correlation on each one. Each sample belongs to one cluster and therefore has only one local label correlation. However, in real-world scenarios, the training samples may have fuzziness and belong to multiple clusters with blended local label correlations, which challenge these works. To solve this problem, we propose in LDL fuzzy label correlation (FLC)—each sample blends, with fuzzy membership, multiple local label correlations. First, we propose two types of FLCs, i.e., fuzzy membership-induced label correlation (FC) and joint fuzzy clustering and label correlation (FCC). Then, we put forward LDL-FC and LDL-FCC to exploit these two FLCs, respectively. Finally, we conduct extensive experiments to justify that LDL-FC and LDL-FCC statistically outperform state-of-the-art LDL methods.