Kernels for identifying patterns in datasets containing noise or transformation invariances
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United States of America Patent
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Jun 26, 2012
Grant Date -
Dec 16, 2010
app pub date -
Aug 25, 2010
filing date -
May 7, 2001
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Abstract
Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map.
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