Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce relational variants of neural topographic maps including the self-organizing map and neural gas, which allow clustering and visualization of data given as pairwise similarities or dissimilarities with continuous prototype updates. It is assumed that the (dis-)similarity matrix originates from Euclidean distances, hover, the underlying embedding of points is unknown. Batch optimization schemes for topographic map formations are formulated in terms of the given (dis-)similarities and convergence is guaranteed, thus providing a way to transfer batch optimization to relational data.
['Alexander Hasenfuss', 'Barbara Hammer']
I am writing a research paper on a idea Relational topographic maps
[{"from": "human", "value": "I am writing a research paper on a idea Relational topographic maps"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce relational variants of neural topographic maps including the self-organizing map and neural gas, which allow clustering and visualization of data given as pairwise similarities or dissimilarities with continuous prototype updates. It is assumed that the (dis-)similarity matrix originates from Euclidean distances, hover, the underlying embedding of points is unknown. Batch optimization schemes for topographic map formations are formulated in terms of the given (dis-)similarities and convergence is guaranteed, thus providing a way to transfer batch optimization to relational data."}]