In this paper, a new manifold learning method to reduce the dimension of hyperspectral data is proposed. In this method, ISOMAP algorithm is used to extract the inherent manifold of hyperspectral data to transform the...
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In this paper, a new manifold learning method to reduce the dimension of hyperspectral data is proposed. In this method, ISOMAP algorithm is used to extract the inherent manifold of hyperspectral data to transform the high-dimensional space into a low-dimensional space. Experiments show that the method is effective, meaningful, and provides a new way for reducing the dimension of hyperspectral data while expands the application area of manifold learning in the hyperspectral data processing filed.
The joint India-USA symposium was held at The Ohio state University in December 2001 and focused on basic research issues and trends in the general area of vibration and noise engineering. Over 100 delegates and obser...
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