Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduced the kernel trick to a linear generative model. In the present study,  trained a single layer model with nonnegativity constraint and expansive nonlinearity. After training,  found that the basis images acquired from natural scenes represented Gabor-like features. Moreover, the distributions of shape parameters of the basis images re similar to those found in V1. Other similar models, such as the sparse coding and the independent component analysis, fail to exhibit these properties.
['Hiroki Yokoyama']
I am writing a research paper on a idea Nonnegative Source Separation with Expansive Nonlinearity: Comparison with the Primary Visual Cortex
[{"from": "human", "value": "I am writing a research paper on a idea Nonnegative Source Separation with Expansive Nonlinearity: Comparison with the Primary Visual Cortex"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduced the kernel trick to a linear generative model. In the present study,  trained a single layer model with nonnegativity constraint and expansive nonlinearity. After training,  found that the basis images acquired from natural scenes represented Gabor-like features. Moreover, the distributions of shape parameters of the basis images re similar to those found in V1. Other similar models, such as the sparse coding and the independent component analysis, fail to exhibit these properties."}]