Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining and pattern recognition area, the learned objects are often represented by the multiple features from various of views. How to learn an efficient and effective feature embedding for the subsequent learning tasks? ,  address this issue by providing a novel multi-task multiview feature embedding (MMFE) framework. The MMFE algorithm is based on the idea of low-rank approximation, which suggests that the observed multiview feature matrix is approximately represented by the low-dimensional feature embedding multiplied by a projection matrix. In order to fully consider the particular role of each view to the multiview feature embedding,  simultaneously suggest the multitask learning scheme and ensemble manifold regularization into the MMFE algorithm to seek the optimal projection. Since the objection function of MMFE is multi-variable and non-convex,  further provide an iterative optimization procedure to find the available solution. Two real world experiments show that the proposed method outperforms single-task-based as ll as state-of-the-art multiview feature embedding methods for the classification problem.
['Qian Zhang', 'Lefei Zhang', 'Bo Du', 'Wei Zheng', 'Wei Bian', 'Dacheng Tao']
I am writing a research paper on a idea MMFE: Multitask Multiview Feature Embedding
[{"from": "human", "value": "I am writing a research paper on a idea MMFE: Multitask Multiview Feature Embedding"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In data mining and pattern recognition area, the learned objects are often represented by the multiple features from various of views. How to learn an efficient and effective feature embedding for the subsequent learning tasks? ,  address this issue by providing a novel multi-task multiview feature embedding (MMFE) framework. The MMFE algorithm is based on the idea of low-rank approximation, which suggests that the observed multiview feature matrix is approximately represented by the low-dimensional feature embedding multiplied by a projection matrix. In order to fully consider the particular role of each view to the multiview feature embedding,  simultaneously suggest the multitask learning scheme and ensemble manifold regularization into the MMFE algorithm to seek the optimal projection. Since the objection function of MMFE is multi-variable and non-convex,  further provide an iterative optimization procedure to find the available solution. Two real world experiments show that the proposed method outperforms single-task-based as ll as state-of-the-art multiview feature embedding methods for the classification problem."}]