Kavli Affiliate: Jing Wang
| First 5 Authors: Zhiqiang Kou, Jing Wang, Yuheng Jia, Xin Geng,
| Summary:
In this paper, we introduce the Dependent Noise-based Inaccurate Label
Distribution Learning (DN-ILDL) framework to tackle the challenges posed by
noise in label distribution learning, which arise from dependencies on
instances and labels. We start by modeling the inaccurate label distribution
matrix as a combination of the true label distribution and a noise matrix
influenced by specific instances and labels. To address this, we develop a
linear mapping from instances to their true label distributions, incorporating
label correlations, and decompose the noise matrix using feature and label
representations, applying group sparsity constraints to accurately capture the
noise. Furthermore, we employ graph regularization to align the topological
structures of the input and output spaces, ensuring accurate reconstruction of
the true label distribution matrix. Utilizing the Alternating Direction Method
of Multipliers (ADMM) for efficient optimization, we validate our method’s
capability to recover true labels accurately and establish a generalization
error bound. Extensive experiments demonstrate that DN-ILDL effectively
addresses the ILDL problem and outperforms existing LDL methods.
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