Application of Locally Linear Embedding Based on Improved Distance in Neuron Classification

Zhen Zhen Wang; Xiao Jun Tong; Shan Zeng

doi:10.4028/www.scientific.net/AMR.926-930.2996

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Application of Locally Linear Embedding Based on...

Application of Locally Linear Embedding Based on Improved Distance in Neuron Classification

Abstract:

For locally linear embedding (LLE) algorithm of the shortcoming, an improved distance algorithm LLE is proposed, in locally linear embedding algorithm the distribution of sample component is different and the Euclidean distance can’t reflect sample distance actually. In the experiment, a sample of 231 neurons is obtained, and the morphological parameters of neurons are calculated firstly. Second, the improved locally linear embedding algorithm is used to reduce data dimensionality. Finally, support vector machine (SVM) algorithm is used to train and test samples. Experimental results show under certain conditions the classification of the method has good classification.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

2996-2999

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.2996

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Online since:

May 2014

Authors:

Zhen Zhen Wang*, Xiao Jun Tong, Shan Zeng

Keywords:

Classification, Linear Locally Embedding Algorithm, Neuron, Support Vector Machine (SVM)

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* - Corresponding Author

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