- Category:
- Graph program
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- File Size:
- 397kb
- Update:
- 2017-03-05
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- Uploaded by:
- fangsm
Description: Based on local linear embedding algorithm Mahalanobis distance metric LLE algorithm (LLE) common Euclidean distance measure of similarity between the samples. For high-dimensional image data, Euclidean distance can not accurately reflect the degree of similarity between samples. In this paper, Mahalanobis distance metric based on local linear embedding algorithm (MLLE). Firstly, learning the existing sample to a Markov measure, then LLE neighbor algorithm selection, existing samples and new samples dimensionality reduction process using Markov measure as a similarity measure would MLLE algorithms and other typical manifold learning algorithm on ORL and USPS to compare experimental results show MLLE algorithm has good recognition performance.
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