An Incremental Locally Linear Embedding Algorithm with Non-Negative Constraints of the Weights

Abstract:

Locally Linear Embedding (LLE) is a batch method. When new sample is added, the whole algorithm must be run repeatedly and all the former computational results are discarded. In the paper, the LLE algorithm processing on new sample points is analyzed. For the insufficient precision of the processing of traditional incremental LLE, an incremental LLE algorithm based on non-negative constraints of the weights is proposed. Non-negative constraints of linear weights are imposed on the new sample points in the projection process. The simple fitting of the original algorithm from the engineering application is avoided by the proposed algorithm and the problem of the constantly updating of the whole manifold is solved at the case of new samples being added. Compared with the traditional incremental LLE method, S-curve simulation data and engineering examples analysis show the feasibility and effectiveness of the proposed algorithm.

• Periodical: Applied Mechanics and Materials (Volume 404)
• Pages: 478-484
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.404.478
• Online since: September 2013
• Authors: Jian Zhong Hu, Qing Cheng Xu
• Keywords: Incremental, Locally Linear Embedding (LLE), Manifold Learning, Non-Negative Constraints

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