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In the course of signal and information processing, the linear processing method is easier to master than the nonlinear process. However, in practical application, the nonlinear problem is often encountered, which is too complex to be limited by the existing signal and information processing resources. Therefore, it is necessary to transform the complex nonlinear problem into a simple linear problem...
We introduce a new algorithm to estimate the manifold dimension of datasets. Our technique is based on the idea that the manifold structure should be preserved as far as possible after dimension reduction. Computing the structure errors of nearest neighbor graphs before and after dimension reduction, we gain the manifold dimension by seeking the smallest value of dimension when the structure error...
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