Affiliations: [a] School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China | [b] Xi’an Key Laboratory of Advanced Control and Intelligent Process, Xi’an, Shaanxi, China | [c] School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an, Shaanxi, China
Correspondence:
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Corresponding authors: Qing Wu, School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, China. E-mail: [email protected]; En Wang, School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi’an 710049, China. E-mail: [email protected].
Abstract: To solve the shortcomings of local linear embedding (LLE), such as sensitive to noise and poor generalization ability for new samples, an improved weighted local linear embedding algorithm based on Laplacian eigenmaps (IWLLE-LE) is proposed in this paper. In the proposed algorithm, Laplacian eigenmaps are used to reconstruct the objective function of dimensionality reduction. The weights of it are introduced by combining the geodesic distance with Euclidean distance, which can effectively represent the manifold structure of nonlinear data. Compared the existing LLE algorithm, the proposed one better maintains the original manifold structure of the data. The merit of the proposal is enhanced by the theoretical analysis and numerical experiments, where the classification recognition rate is 2%–8% higher than LLE.
Keywords: Local linear embedding, Laplace feature, map geodesic distance, manifold structure
