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Improving Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2009, 14(8), pp.10-18
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Young-Tae Kwak 1 Shin Jung Hoon 1

1전북대학교

Accredited

ABSTRACT

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix, the Levenberg-Marquardt learning must increase or decrease and recalculate the inverse matrix of the Jacobian matrix due to these changes of . Therefore, to have the proper , we create the principal submatrix of Jacobian matrix and set the as the eigenvalues sum of the principal submatrix, which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

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