Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  proposed an adaptive ensemble manifold learning (AEML) for the neuroimaging retrieval via multimodality data. Specifically,  investigated the complementarity por of the multimodality data to index brain image disorder with different ights. Unlike the previous manifold learning methods,  develop a new learning method to incorporate the geometrical information and impose the similarity constraints. In addition, a generalized norm is also introduced to control the sparseness degree adaptively. Finally, a novel objective function is devised to retrieve the similar brain disorder in a database by a multi-task learning framework.  evaluated the proposed AEML algorithm via the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database with 805 pre-diagnosed subjects. Experimental results show our algorithm achieved promising results and outperformed several related algorithms.
['Yinan Zhuo', 'Dong Ni', 'Siping Chen', 'Bai Ying Lei', 'Tianfu Wang']
I am writing a research paper on a idea Adaptive ensemble manifold learning for neuroimaging retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive ensemble manifold learning for neuroimaging retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  proposed an adaptive ensemble manifold learning (AEML) for the neuroimaging retrieval via multimodality data. Specifically,  investigated the complementarity por of the multimodality data to index brain image disorder with different ights. Unlike the previous manifold learning methods,  develop a new learning method to incorporate the geometrical information and impose the similarity constraints. In addition, a generalized norm is also introduced to control the sparseness degree adaptively. Finally, a novel objective function is devised to retrieve the similar brain disorder in a database by a multi-task learning framework.  evaluated the proposed AEML algorithm via the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database with 805 pre-diagnosed subjects. Experimental results show our algorithm achieved promising results and outperformed several related algorithms."}]