Parameters Selection of LLE Algorithm for Classification Tasks
2014 ◽
Vol 1037
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pp. 422-427
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The crux in the locally linear embedding algorithm or LLE is the selection of embedding dimensionality and neighborhood size. A method of parameters selection based on the normalized cut criterion or Ncut for classification tasks is proposed. Differing from current techniques based on the neighborhood topology preservation criterion, the proposed method capitalizes on class separability of embedding result. By taking it into consideration, the intrinsic capability of LLE can be more faithfully reflected, and hence more rational features for classification in real-life applications can be offered. The theoretical argument is supported by experimental results from synthetic and real data sets.
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2021 ◽
pp. 943-961
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1993 ◽
Vol 18
(1)
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pp. 41-68
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