Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many mistakes. In this work,  aim to learn a distance metric from noisy constraints by robust optimization in a worst-case scenario, to which  refer as robust metric learning.  formulate the learning task initially as a combinatorial optimization problem, and show that it can be elegantly transformed to a convex programming problem.  present an efficient learning algorithm based on smooth optimization [7]. It has a worst-case convergence rate of O(1/√e) for smooth optimization problems, where e is the desired error of the approximate solution. Finally, our empirical study with UCI data sets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.
['Kaizhu Huang', 'Rong Jin', 'Z.Z. Xu', 'Cheng-Lin Liu']
I am writing a research paper on a idea Robust metric learning by smooth optimization
[{"from": "human", "value": "I am writing a research paper on a idea Robust metric learning by 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: Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many mistakes. In this work,  aim to learn a distance metric from noisy constraints by robust optimization in a worst-case scenario, to which  refer as robust metric learning.  formulate the learning task initially as a combinatorial optimization problem, and show that it can be elegantly transformed to a convex programming problem.  present an efficient learning algorithm based on smooth optimization [7]. It has a worst-case convergence rate of O(1/\u221ae) for smooth optimization problems, where e is the desired error of the approximate solution. Finally, our empirical study with UCI data sets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods."}]