The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Performance Improvement of Independent Component Analysis by Fixed-point Algorithm of Adaptive Learning Parameters

적응적 학습 파라미터의 고정점 알고리즘에 의한 독립성분분석의 성능개선

  • 조용현 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 민성재 ((주)MTIS 연구원)
  • Published : 2003.08.01
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Abstract

This paper proposes an efficient fixed-point (FP) algorithm for improving performances of the independent component analysis (ICA) based on neural networks. The proposed algorithm is the FP algorithm based on Newton method for ICA using the adaptive learning parameters. The purpose of this algorithm is to improve the separation speed and performance by using the learning parameters in Newton method, which is based on the first order differential computation of entropy optimization function. The learning rate and the moment are adaptively adjusted according to an updating state of inverse mixing matrix. The proposed algorithm has been applied to the fingerprints and the images generated by random mixing matrix in the 8 fingerprints of 256${\times}$256-pixel and the 10 images of 512$\times$512-pixel, respectively. The simulation results show that the proposed algorithm has the separation speed and performance better than those using the conventional FP algorithm based on Newton method. Especially, the proposed algorithm gives relatively larger improvement degree as the problem size increases.

본 연구에서는 뉴우턴법의 고정점 알고리즘에 적응 조정이 가능한 학습 파라미터를 이용한 효율적인 신경망 기반 독립성분분석기법을 제안하였다. 이는 엔트로피 최적화 함수의 1차 미분을 이용하는 뉴우턴법의 고정점 알고리즘에서 학습율과 모멘트를 역혼합행렬의 경신 상태에 따나 적응조정되도록 함으로써 분리속도와 분리성능을 개선시키기 위함이다 제안된 기법을 256$\times$256 픽셀의 8개 지문과 512$\times$512 픽셀의 10개 영상으로부터 임의의 혼합행렬에 따라 발생되는 지문과 영상의 분리에 적용한 결과, 기존의 고정점 알고리즘에 의한 결과보다 우수한 분리성능과 빠른 분리속도가 있음을 확인하였다. 특히 제안된 알고리즘은 문제의 규모가 클수록 분리성능과 분리속도의 개선 정도가 큼을 확인하였다.

Keywords

References

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