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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

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
Outlines

  • Motivation

  • Objectives

  • Methodology

  • Experiments

  • Conclusions

  • Comments


Motivation
Motivation

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


Objectives
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.


Methodology
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.


Methodology1
Methodology

  • Matrix Learning in GLVQ

    • Matrix learning in GLVQ is derived as a minimization of the cost function


Methodology2
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…


Experiments
Experiments

Artificial Data


Experiments1
Experiments

  • Real-Life Data

    • Pima Indians Diabetes


Experiments2
Experiments

  • Real-Life Data

    • Glass Identification


Experiments3
Experiments

  • Real-Life Data

    • Letter Recognition


Conclusions
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.


Comments
Comments

  • Advantages

    • Improving the VQ in the ANN.

  • Drawbacks

    • It’s very difficult to understand.

  • Applications

    • Learning Vector Quantization


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