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Regularization in Matrix Relevance Learning

Regularization in Matrix Relevance Learning. Petra Schneider, Kerstin Bunte , Han Stiekema , Barbara Hammer, Thomas Villmann , and Michael Biehl TNN, 2010 Presented by Hung-Yi Cai 2011/6/29. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Regularization in Matrix Relevance Learning

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  1. Regularization in Matrix Relevance Learning Petra Schneider, Kerstin Bunte, Han Stiekema, Barbara Hammer, Thomas Villmann, and Michael Biehl TNN, 2010 Presented by Hung-Yi Cai 2011/6/29

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Matrix learning tends to perform an overly strong feature selection which may have negative impact on the classification performance and the learning dynamics.

  4. Objectives To propose a regularization scheme for metric adaptation methods in LVQ to prevent the algorithms from oversimplifying the distance measure. The standard motivation for regularization is to prevent a learning system from overfitting.

  5. Methodology • Matrix Learning in LVQ • LVQ aims at parameterizing a distance-based classification scheme in terms of prototypes. • Learning aims at determining weight locations for the prototypes such that the given training data are mapped to their corresponding class labels.

  6. Methodology • Matrix Learning in GLVQ • Matrix learning in GLVQ is derived as a minimization of the cost function

  7. Methodology • Regularized cost function • The approach can easily be applied to any LVQ algorithm with an underlying cost function . • In case of GMLVQ, the extended cost function… • The update rule for the metric parameters…

  8. Experiments Artificial Data

  9. Experiments • Real-Life Data • Pima Indians Diabetes

  10. Experiments • Real-Life Data • Glass Identification

  11. Experiments • Real-Life Data • Letter Recognition

  12. Conclusions • The proposed regularization scheme prevents oversimplification, eliminates instabilities in the learning dynamics, and improves the generalization ability of the considered metric adaptation algorithms. • The new method turns out to be advantageous to derive discriminative visualizationsby means of GMLVQ with a rectangular matrix.

  13. Comments • Advantages • Improving the VQ in the ANN. • Drawbacks • It’s very difficult to understand. • Applications • Learning Vector Quantization

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