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 framework to tackle the challenges posed by noise in label distribution learning%2C 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%2C we develop a linear mapping from instances to their true label distributions%2C incorporating label correlations%2C and decompose the noise matrix using feature and label representations%2C applying group sparsity constraints to accurately capture the noise. Furthermore%2C we employ graph regularization to align the topological structures of the input and output spaces%2C ensuring accurate reconstruction of the true label distribution matrix. Utilizing the Alternating Direction Method of Multipliers (ADMM) for efficient optimization%2C 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.?quality=80&w=800)
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