Locally Linear Embedding based on Image Euclidean Distance

In this work, a feature extraction approach based on improved Locally Linear Embedding(LLE) is proposed. In the algorithm, tangent space distance is introduced to LLE, which overcomes the shortcoming of original LLE method based on Euclidean distance. It can satisfy the requirement of locally linear much better and so can express the I/O mapping quality better than classical method. Simulation results are given to demonstrate the effectiveness of the improved LLE method.

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Handwritten Digit Recognition Based on Modified LLE Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2290 ◽

2014 ◽

Vol 602-605 ◽

pp. 2290-2293

Author(s):

Bo Yu Gu ◽

Ye Chun Li

Keyword(s):

Euclidean Distance ◽

Locally Linear Embedding ◽

Learning Approach ◽

Affine Transformations ◽

Accuracy Rate ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit ◽

Linear Embedding ◽

Locally Linear

Locally linear embedding is an efficient manifold learning approach. A modified locally linear embedding algorithm is proposed to cope with the interferences of affine transformations in handwritten digit recognition. In order to offset all kinds of affine transformations, the Euclidean distance is replaced by the tangent distance which is more appropriate for handwritten digit recognition based on image. And the number of neighborhood is computed automatically based on the similarity of images. Experimental results show that the accuracy rate of is improved.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2996 ◽

2014 ◽

Vol 926-930 ◽

pp. 2996-2999

Author(s):

Zhen Zhen Wang ◽

Xiao Jun Tong ◽

Shan Zeng

Keyword(s):

Euclidean Distance ◽

Locally Linear Embedding ◽

Support Vector ◽

Morphological Parameters ◽

Svm Algorithm ◽

Good Classification ◽

Linear Embedding ◽

Sample Component ◽

Locally Linear

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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