Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study the problem of learning a low-rank (sparse) distance matrix.  propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixed-norm regularization which is non-convex.  then show that it can be equivalently formulated as a convex saddle (min-max) problem. From this saddle representation,  develop an efficient smooth optimization approach [17] for sparse metric learning, although the learning model is based on a non-differentiable loss function. Finally,  run experiments to validate the effectiveness and efficiency of our sparse metric learning model on various datasets.
['Yiming Ying', 'Kaizhu Huang', 'Colin Campbell']
I am writing a research paper on a idea Sparse Metric Learning via Smooth Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Sparse Metric Learning via Smooth Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study the problem of learning a low-rank (sparse) distance matrix.  propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixed-norm regularization which is non-convex.  then show that it can be equivalently formulated as a convex saddle (min-max) problem. From this saddle representation,  develop an efficient smooth optimization approach [17] for sparse metric learning, although the learning model is based on a non-differentiable loss function. Finally,  run experiments to validate the effectiveness and efficiency of our sparse metric learning model on various datasets."}]