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Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session I - Machine Learning
Improved Locally Linear Embedding by Using Adaptive Neighborhood Selection Techniques
Z. Zhang,* J. Zhou, H. Shao, A. Bao
*corresponding author
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Pre-published on: 2017 July 17
Published on: 2017 September 06
Unsupervised learning algorithm locally linear
embedding (LLE) is a typical technique which
applies the preserving embedding method of high dimensional data to low dimension. The number of neighborhood nodes of LLE is a decisive parameter because the improper value will affect the manifold structure in the local neighborhood and lead to the lower computational
efficiency. Based on the fact that the shortest path in low-density can be established easily, this paper proposes an improved LLE method by using the sparse matrix in combination with the weights related to each point used for the linear combination in local neighborhood. The correlation dimension between high and low dimension is used to estimate the proper number of the reduced dimension, thereby selecting the best upper bound for the non-uniform manifold. Finally, we provide the experimental evaluation to verify the effectiveness of the proposed
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