Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable,  reformulate the problem to eliminate the need for general semidefinite programming. In particular,  provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates.  evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.
['Novi Quadrianto', 'John Lim', 'Dale Schuurmans', 'Tibério S. Caetano']
I am writing a research paper on a idea Convex Relaxation of Mixture Regression with Efficient Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Convex Relaxation of Mixture Regression with Efficient Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable,  reformulate the problem to eliminate the need for general semidefinite programming. In particular,  provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates.  evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data."}]