Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of incomplete labels is frequently encountered in many application domains where the training labels are obtained via crowd-sourcing. The label incompleteness significantly increases the difficulty of acquiring accurate multi-label prediction models. ,  propose a novel semi-supervised multi-label method that integrates low-rank label matrix recovery into the manifold regularized vector-valued prediction framework to address multi-label learning with incomplete labels. The proposed method is formulated as a convex but non-smooth joint optimization problem over the latent label matrix and the prediction model parameters.  then develop a fast proximal gradient descent with continuation algorithm to solve it for a global optimal solution. The efficacy of the proposed approach is demonstrated on multiple multi-label datasets, comparing to related methods that handle incomplete labels.
['Feipeng Zhao', 'Yuhong Guo']
I am writing a research paper on a idea Semi-supervised multi-label learning with incomplete labels
[{"from": "human", "value": "I am writing a research paper on a idea Semi-supervised multi-label learning with incomplete labels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of incomplete labels is frequently encountered in many application domains where the training labels are obtained via crowd-sourcing. The label incompleteness significantly increases the difficulty of acquiring accurate multi-label prediction models. ,  propose a novel semi-supervised multi-label method that integrates low-rank label matrix recovery into the manifold regularized vector-valued prediction framework to address multi-label learning with incomplete labels. The proposed method is formulated as a convex but non-smooth joint optimization problem over the latent label matrix and the prediction model parameters.  then develop a fast proximal gradient descent with continuation algorithm to solve it for a global optimal solution. The efficacy of the proposed approach is demonstrated on multiple multi-label datasets, comparing to related methods that handle incomplete labels."}]