# Difference between revisions of "learning a Nonlinear Embedding by Preserving Class Neighborhood Structure"

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The paper <ref>Salakhutdinov, R., & Hinton, G. E. (2007). Learning a nonlinear embedding by preserving class neighbourhood structure. AI and Statistics.</ref> presented here describes a method to learn a nonlinear transformation from the input space to a low-dimensional | The paper <ref>Salakhutdinov, R., & Hinton, G. E. (2007). Learning a nonlinear embedding by preserving class neighbourhood structure. AI and Statistics.</ref> presented here describes a method to learn a nonlinear transformation from the input space to a low-dimensional | ||

feature space in which K-nearest neighbour classification performs well. As the performance of algorithms like K-nearest neighbours (KNN) that are based on computing distances, the main objective of the proposed algorithm is to learn a good similarity measure that can provide insight into how high-dimensional data is organized. The nonlinear transformation is learned by pre-training and fine-tuning a multilayer neural network. The authors also show how to enhance the performance of non-linear transformation further using unlabeled data. Experimental results on a widely used version of the MNIST handwritten digit recognition task show that proposed algorithm achieves a much lower error rate than SVM or standard backpropagation. | feature space in which K-nearest neighbour classification performs well. As the performance of algorithms like K-nearest neighbours (KNN) that are based on computing distances, the main objective of the proposed algorithm is to learn a good similarity measure that can provide insight into how high-dimensional data is organized. The nonlinear transformation is learned by pre-training and fine-tuning a multilayer neural network. The authors also show how to enhance the performance of non-linear transformation further using unlabeled data. Experimental results on a widely used version of the MNIST handwritten digit recognition task show that proposed algorithm achieves a much lower error rate than SVM or standard backpropagation. | ||

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==References== | ==References== | ||

<references/> | <references/> |

## Revision as of 20:36, 30 June 2009

# Introduction

The paper <ref>Salakhutdinov, R., & Hinton, G. E. (2007). Learning a nonlinear embedding by preserving class neighbourhood structure. AI and Statistics.</ref> presented here describes a method to learn a nonlinear transformation from the input space to a low-dimensional feature space in which K-nearest neighbour classification performs well. As the performance of algorithms like K-nearest neighbours (KNN) that are based on computing distances, the main objective of the proposed algorithm is to learn a good similarity measure that can provide insight into how high-dimensional data is organized. The nonlinear transformation is learned by pre-training and fine-tuning a multilayer neural network. The authors also show how to enhance the performance of non-linear transformation further using unlabeled data. Experimental results on a widely used version of the MNIST handwritten digit recognition task show that proposed algorithm achieves a much lower error rate than SVM or standard backpropagation.

## References

<references/>