使用广义线性模型和加法模型估计分类图像。
Estimating classification images with generalized linear and additive models.
作者信息
Knoblauch Kenneth, Maloney Laurence T
机构信息
Stem Cell and Brain Research Institute, Département Neurosciences Intégratives, Bron, France.
出版信息
J Vis. 2008 Dec 22;8(16):10.1-19. doi: 10.1167/8.16.10.
Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.
用于对分类图像数据进行建模的传统方法可以用标准线性模型（LM）来描述。我们展示了如何将该问题表征为具有伯努利分布的广义线性模型（GLM）。我们通过模拟证明，在没有内部噪声的情况下，这种方法在估计基础模板方面更准确。然而，随着内部噪声的增加，GLM相对于LM的优势会降低，并且GLM并不比LM更准确。然后我们引入广义相加模型（GAM），它是GLM的一种扩展，可用于自适应地估计平滑分类图像。我们表明这种方法对内部噪声的存在更具鲁棒性，最后，我们证明GAM很容易适用于高阶（非线性）分类图像的估计及其显著性测试。
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