Estimation of Signal Information Content for Classification
Estimation of Signal Information Content for Classification
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DOI：
10.1109/dsp.2009.4785948
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发表时间：
2009-02
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影响因子：
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通讯作者：
John W. Fisher III;Michael R. Siracusa;Kinh H. Tieu
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文献类型：
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作者：
John W. Fisher III;Michael R. Siracusa;Kinh H. Tieu
长期以来，人们一直在假设检验的背景下研究信息度量，从而导致基于信号的信息内容或分布之间的分歧的各种性能界限。在这里，我们考虑的问题，估计的信息内容的高维信号的分类的目的。直接估计高维信号的信息通常是不容易处理的，因此我们考虑对[1]中首次提出的方法的扩展，其中高维信号被映射到低维特征空间，从而产生信息内容的下限。我们开发了一个仿射不变梯度方法，并检查所得到的估计预测分类性能经验的效用。
Information measures have long been studied in the context of hypothesis testing leading to variety of bounds on performance based on the information content of a signal or the divergence between distributions. Here we consider the problem of estimation of information content for high-dimensional signals for purposes of classification. Direct estimation of information for high-dimensional signals is generally not tractable therefore we consider an extension to a method first suggested in [1] in which high dimensional signals are mapped to lower dimensional feature spaces yielding lower bounds on information content. We develop an affine-invariant gradient method and examine the utility of the resulting estimates for predicting classification performance empirically.