Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship beten two datasets. Hover, most of algorithm implementations of PLSR may only achieve a suboptimal solution through an optimization on the Euclidean space. ,  propose several novel PLSR models on Riemannian manifolds and develop optimization algorithms based on Riemannian geometry of manifolds. This algorithm can calculate all the factors of PLSR globally to avoid suboptimal solutions. In a number of experiments,  have demonstrated the benefits of applying the proposed model and algorithm to a variety of learning tasks in pattern recognition and object classification.
['Haoran Chen', 'Yanfeng Sun', 'Junbin Gao', 'Yongli Hu', 'Baocai Yin']
I am writing a research paper on a idea Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
[{"from": "human", "value": "I am writing a research paper on a idea Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship beten two datasets. Hover, most of algorithm implementations of PLSR may only achieve a suboptimal solution through an optimization on the Euclidean space. ,  propose several novel PLSR models on Riemannian manifolds and develop optimization algorithms based on Riemannian geometry of manifolds. This algorithm can calculate all the factors of PLSR globally to avoid suboptimal solutions. In a number of experiments,  have demonstrated the benefits of applying the proposed model and algorithm to a variety of learning tasks in pattern recognition and object classification."}]