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- W2379600288 abstract "Locally Linear Embedding(LLE) algorithm can achieve data dimensionality reduction by finding the local linear low-dimensional manifold hidden in the high-dimensional space.The LLE algorithm is sensitive to noise,moreover,the stability of the algorithm is very poor when exposed to strong noise.This paper proposes a sparse constraint improvement method,by adding L1 norm punitive constraint to the reconstruction error function,the optimal reconstruction weight matrix is sparser.Transform the reconstruction error function which adds the sparse constraint into a general quadratic programming problem by regularization,and uses interior-point iteration method to search the optimal solution quickly.Simulation experimental results of the typical high-dimensional data set dimensionality reduction show that the dimensionality reduction results of the LLE algorithm in sparse constraint is significantly better than the classic LLE algorithm under the influence of different noise,and has a stronger ability in resisting noise." @default.
- W2379600288 created "2016-06-24" @default.
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- W2379600288 date "2013-01-01" @default.
- W2379600288 modified "2023-09-24" @default.
- W2379600288 title "Improved LLE Algorithm Based on Sparse Constraint" @default.
- W2379600288 hasPublicationYear "2013" @default.
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