Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a method for finding an $$L_q$$Lq-closest-point to a set of affine subspaces, that is a point for which the sum of the q-th por of orthogonal distances to all the subspaces is minimized, where $$1 \le q < 2$$1≤q<2.  give a theoretical proof for the convergence of the proposed algorithm to a unique $$L_q$$Lq minimum. The proposed method is motivated by the $$L_q$$Lq iszfeld algorithm, an extremely simple and rapid averaging algorithm, that finds the $$L_q$$Lq mean of a set of given points in a Euclidean space. The proposed algorithm is applied to the triangulation problem in computer vision by finding the $$L_q$$Lq-closest-point to a set of lines in 3D. Our experimental results for the triangulation problem confirm that the $$L_q$$Lq-closest-point method, for $$1 \le q < 2$$1≤q<2, is more robust to outliers than the $$L_2$$L2-closest-point method.
['Khurrum Aftab', 'Richard I. Hartley', 'Jochen Trumpf']
I am writing a research paper on a idea $${L_q}$$Lq-Closest-Point to Affine Subspaces Using the Generalized Weiszfeld Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea $${L_q}$$Lq-Closest-Point to Affine Subspaces Using the Generalized Weiszfeld Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a method for finding an $$L_q$$Lq-closest-point to a set of affine subspaces, that is a point for which the sum of the q-th por of orthogonal distances to all the subspaces is minimized, where $$1 \\le q < 2$$1\u2264q<2.  give a theoretical proof for the convergence of the proposed algorithm to a unique $$L_q$$Lq minimum. The proposed method is motivated by the $$L_q$$Lq iszfeld algorithm, an extremely simple and rapid averaging algorithm, that finds the $$L_q$$Lq mean of a set of given points in a Euclidean space. The proposed algorithm is applied to the triangulation problem in computer vision by finding the $$L_q$$Lq-closest-point to a set of lines in 3D. Our experimental results for the triangulation problem confirm that the $$L_q$$Lq-closest-point method, for $$1 \\le q < 2$$1\u2264q<2, is more robust to outliers than the $$L_2$$L2-closest-point method."}]