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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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