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Related Experiment Video

Updated: Jun 7, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
08:51

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

Kernel Wiener filter and its application to pattern recognition.

Hirokazu Yoshino1, Chen Dong, Yoshikazu Washizawa

  • 1Research Center, Asahi Glass Company Ltd., Tsurumi, Yokohama, Japan. hirokazu-yoshino@agc.co.jp

IEEE Transactions on Neural Networks
|November 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Kernel Wiener Filter (KWF) that efficiently handles noise as a random variable, reducing computational costs. The enhanced KWF improves image denoising and classification accuracy, even in noisy conditions.

Related Experiment Videos

Last Updated: Jun 7, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
08:51

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

Area of Science:

  • Signal Processing
  • Machine Learning
  • Computational Statistics

Background:

  • The Wiener filter (WF) is a standard linear operator for inverse problems, estimating signals by minimizing mean squared error.
  • Kernel methods, like the Kernel Wiener Filter (KWF), offer non-linear extensions but face high computational costs when handling noise via sampling.
  • Existing KWF approaches require noise to be processed through samples, leading to significant computational burden with large datasets.

Purpose of the Study:

  • To develop an efficient Kernel Wiener Filter (KWF) that overcomes the sampling-based noise handling limitations of traditional methods.
  • To introduce a novel error estimation technique for kernel filters using first-order approximations.
  • To demonstrate the applicability and advantages of the proposed KWF in image denoising and classification tasks.

Main Methods:

  • Utilized first-order approximation of kernel functions to treat additive noise as a random variable, avoiding sample-based computation.
  • Developed an approximation-based error estimation method for kernel filters.
  • Applied the proposed KWF to image denoising and various classification tasks, including binary, multiclass, and noise-robust classification.

Main Results:

  • The proposed KWF significantly reduces computational complexity compared to sample-based methods.
  • Achieved effective image denoising and accurate error estimation using the approximation-based methods.
  • Demonstrated competitive or superior performance in classification tasks, including those with inherent noise, by leveraging the noise term for regularization.

Conclusions:

  • The first-order approximation enables efficient, sample-free noise handling in KWF, reducing computational load.
  • The developed KWF and error estimation methods offer practical advantages for signal processing and machine learning applications.
  • KWF shows promise for robust classification, particularly in noisy environments, by providing an approximation to the maximum a posteriori classifier.