Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a greedy algorithm that works from coarse to fine by iteratively applying localized principal component analysis (PCA). The decision where and when to split or add new components is based on two antagonistic criteria. Firstly, the ll known quadratic reconstruction error and secondly a measure for the homogeneity of the distribution. For the latter criterion, which  call “generation error”,  compared two different possible methods to assess if the data samples are distributed homogeneously. The proposed algorithm does not involve a costly multi-objective optimization to find a partition of the inputs. Further, the final number of local PCA units, as ll as their individual dimensionality need not to be predefined.  demonstrate that the method can flexibly react to different intrinsic dimensionalities of the data.
['S. N. John', 'Heiko Wersing', 'Helge Ritter']
I am writing a research paper on a idea An iterative approach to local-PCA
[{"from": "human", "value": "I am writing a research paper on a idea An iterative approach to local-PCA"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a greedy algorithm that works from coarse to fine by iteratively applying localized principal component analysis (PCA). The decision where and when to split or add new components is based on two antagonistic criteria. Firstly, the ll known quadratic reconstruction error and secondly a measure for the homogeneity of the distribution. For the latter criterion, which  call \u201cgeneration error\u201d,  compared two different possible methods to assess if the data samples are distributed homogeneously. The proposed algorithm does not involve a costly multi-objective optimization to find a partition of the inputs. Further, the final number of local PCA units, as ll as their individual dimensionality need not to be predefined.  demonstrate that the method can flexibly react to different intrinsic dimensionalities of the data."}]