Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic.

IEEE Transactions on Image Processing(2018)

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摘要
Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parame...
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关键词
Probability density function,Training data,Feature extraction,Pattern recognition,Nearest neighbor methods
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