Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection

The paper deals with the concept of relevance learning in learning vector quantization. Recent approaches are considered: the generalized learning vector quantization as well as the soft learning vector quantization. It is shown that relevance learning can be included in both methods obtaining similar structured learning rules for prototype learning as well as relevance factor adaptation. We show the power in case of image classification of natural environment images. The performance makes the tool suitable for classification tasks in robotics.

[1]  Barbara Hammer,et al.  Supervised Neural Gas for Learning Vector Quantization , 2002 .

[2]  Koby Crammer,et al.  Margin Analysis of the LVQ Algorithm , 2002, NIPS.

[3]  Samuel Kaski,et al.  Bankruptcy analysis with self-organizing maps in learning metrics , 2001, IEEE Trans. Neural Networks.

[4]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[5]  Samuel Kaski,et al.  A Topography-Preserving Latent Variable Model with Learning Metrics , 2001, WSOM.

[6]  Thomas Villmann,et al.  Neural maps in remote sensing image analysis , 2003, Neural Networks.

[7]  Klaus Obermayer,et al.  Soft nearest prototype classification , 2003, IEEE Trans. Neural Networks.

[8]  Harold J. Kushner,et al.  wchastic. approximation methods for constrained and unconstrained systems , 1978 .

[9]  Barbara Hammer,et al.  Relevance determination in Learning Vector Quantization , 2001, ESANN.

[10]  Thomas Villmann,et al.  On the Generalization Ability of GRLVQ Networks , 2005, Neural Processing Letters.

[11]  Panu Somervuo,et al.  Self-Organizing Maps and Learning Vector Quantization for Feature Sequences , 1999, Neural Processing Letters.

[12]  Samuel Kaski,et al.  The Adaptive-Subspace Self-Organizing Map (ASSOM) , 1997 .

[13]  Atsushi Sato,et al.  Generalized Learning Vector Quantization , 1995, NIPS.

[14]  Atsushi Sato,et al.  A formulation of learning vector quantization using a new misclassification measure , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[15]  A. Sato,et al.  An Analysis of Convergence in Generalized LVQ , 1998 .

[16]  Barbara Hammer,et al.  Prototype based recognition of splice sites , 2005 .

[17]  Gert Pfurtscheller,et al.  Automated feature selection with a distinction sensitive learning vector quantizer , 1996, Neurocomputing.

[18]  H. Ritter Self-Organizing Maps on non-euclidean Spaces , 1999 .

[19]  Thomas Villmann,et al.  Generalized relevance learning vector quantization , 2002, Neural Networks.

[20]  Klaus Obermayer,et al.  Soft Learning Vector Quantization , 2003, Neural Computation.