Adaptive Combination of PCA and VQ Networks

Andreas Weingessel; Horst Bischof; Kurt Hornik; Friedrich Leisch

doi:10.1109/72.623222

Adaptive Combination of PCA and VQ Networks

Andreas Weingessel, Horst Bischof, Kurt Hornik, Friedrich Leisch

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.

Original language	English
Pages (from-to)	1208-1211
Journal	IEEE Transactions on Neural Networks
Volume	8
Issue number	5
DOIs	https://doi.org/10.1109/72.623222
Publication status	Published - 1997
Externally published	Yes

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title = "Adaptive Combination of PCA and VQ Networks",

abstract = "In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.",

author = "Andreas Weingessel and Horst Bischof and Kurt Hornik and Friedrich Leisch",

year = "1997",

doi = "10.1109/72.623222",

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AU - Weingessel, Andreas

AU - Bischof, Horst

AU - Hornik, Kurt

AU - Leisch, Friedrich

PY - 1997

Y1 - 1997

N2 - In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.

AB - In this paper we consider the principal component analysis (PCA) and vector quantization (VQ) neural networks for image compression. We present a method where the PCA and VQ steps are adaptively combined. A learning algorithm for this combined network is derived. We demonstrate that this approach can improve the results of the successive application of the individually optimal methods.

U2 - 10.1109/72.623222

DO - 10.1109/72.623222

M3 - Article

SN - 1941-0093

VL - 8

SP - 1208

EP - 1211

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 5

ER -

Adaptive Combination of PCA and VQ Networks

Abstract

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