Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   introduce a coupled Helmholtz machine for principal component analysis (PCA), where sub-machines are related through sharing some latent variables and associated ights.  present a wake-sleep algorithm for PCA (referred to as WS-PCA), leading both generative and recognition ights to converge to principal eigenvectors of a data covariance matrix without rotational ambiguity, in contrast to probabilistic PCA and EM-PCA. Then  also present a kernerlized variation, i.e., a wake-sleep algorithm for kernel PCA (WS-KPCA). The coupled Helmholtz machine provides a unified view of principal component analysis, including various existing algorithms as its special cases. The validity of wake-sleep PCA and KPCA algorithms are confirmed by numerical experiments.
['Seungjin Choi']
I am writing a research paper on a idea Wake-Sleep PCA
[{"from": "human", "value": "I am writing a research paper on a idea Wake-Sleep 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 coupled Helmholtz machine for principal component analysis (PCA), where sub-machines are related through sharing some latent variables and associated ights.  present a wake-sleep algorithm for PCA (referred to as WS-PCA), leading both generative and recognition ights to converge to principal eigenvectors of a data covariance matrix without rotational ambiguity, in contrast to probabilistic PCA and EM-PCA. Then  also present a kernerlized variation, i.e., a wake-sleep algorithm for kernel PCA (WS-KPCA). The coupled Helmholtz machine provides a unified view of principal component analysis, including various existing algorithms as its special cases. The validity of wake-sleep PCA and KPCA algorithms are confirmed by numerical experiments."}]